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


Authors : Obasi, Richard Ubadire; Okonkwo, Innocent I.; Obinwa, C. I.

Volume/Issue : Volume 10 - 2025, Issue 8 - August


Google Scholar : https://tinyurl.com/yrpb4h5e

Scribd : https://tinyurl.com/2tv2m4rs

DOI : https://doi.org/10.38124/ijisrt/25aug680

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Note : Google Scholar may take 30 to 40 days to display the article.


Abstract : 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.

Keywords : Load Flow, ANN Based Unified Power Flow Controller, Power Loss and Reactive Power Loss.

References :

  1. Badran, O. Mekhilef, H. & Mokhlis, W. D. (2017). “Optimal reconfiguration of distribution system connected with distributed generations: A review of different methodologies” Renew and Sustain. Energy Rev. Vol. 72, pp. 854-867.
  2. Bazilah, I., Mohammed, L. O., Kanendra N. V. & Muhammad M. N. (2017). “A comprehensive Review on Optimal location and sizing of Reactive Power Compensation using Hybrid-Based Approaches for power loss reduction, Voltage stability improvement, Voltage Enhancement and Loadability Enhancement IEEE Transaction.
  3. Ejebe, G.C. & Wollenberg, B.F. (1979). “Automatic contingency selection” IEEE Trans. On power Apparatus and Syst. Pps- 98, 97.
  4. Ezechukwu, O. A., Chukwuagu, M. I., & Ezendiokwelu, C. E.  (2022). Evaluation of the Performance of a loss minimization method using ANN based UPFC. International Journal of Innovative Science and Research Technology. Volume. 7, Issue 3, ISSN No: 2456-2165
  5. Ezeonye, C.S. Atuchukwu, A.J. and Okonkwo, I.I. (2024). Comparative Effect of Series and Shunt FACTS on the steady state Improvement of Voltage Profile of the Nigerian 330KV Transmission System. Journal of Science and Technology Research 6(2); pp. 31-42.
  6. Nwohu M. N., Isah A., Usman A. U. and Sadiq A. A. (2016). Optimal Placement of Thyristor Controlled Series Compensator (TCSC) on Nigerian 330kV Transmission Grid to Minimize Real Power Losses. International Journal of Research Studies in Electrical and Electronics Engineering, ISSN 2454-9436
  7. Obi, P.I., Okonkwo I.I. & Ogba C.O. (2022). Power Supply Enhancement in Onitsha Distribution Network Using Distribution Generations. Nigerian Journal of Technology (NIJOTECH) 41(2); 318-329.
  8. Okonkwo et al., (2020). Technical losses mitigation in 330KV Nigeria transmission network system. International Research Journal of Modernization in Engineering Technology and Science. Volume 2, Issue 12; Pp. 1076-1098.
  9. Siti, A. J., lsmail, M, & Muhammad, M. O. (2016) transmission loss minimization using SVC Based on particle swarm optimization, IEEE symposium on industrial electronics and applications(ISEA 2016), Langkawi Malaysia, vol.34, No.87, pp.35-78.
  10. Ulasi, A. J. Iloh, J. P. and P. I. Obi, (2019)“Application of Linear Sensitivity Factors for Real Time Power System Post Contingency Flow”, Iconic Research and Engineering Journals, Vol. 2, No. 11, pp. 46-61, 2019.

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.

Keywords : Load Flow, ANN Based Unified Power Flow Controller, Power Loss and Reactive Power Loss.

CALL FOR PAPERS


Paper Submission Last Date
30 - November - 2025

Video Explanation for Published paper

Never miss an update from Papermashup

Get notified about the latest tutorials and downloads.

Subscribe by Email

Get alerts directly into your inbox after each post and stay updated.
Subscribe
OR

Subscribe by RSS

Add our RSS to your feedreader to get regular updates from us.
Subscribe