Comparative Analysis of Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System Techniques for Fault Detection and Mitigation on the Nigerian 330 kV Transmission Network

References

[1] P. Kundur, Power System Stability and Control. New York, NY, USA: McGraw-Hill, 1994.

[2] Y. Dash, A. Abraham, N. Kumar, and M. Raj, “Role of artificial intelligence in transmission line protection: A review of three decades of research,” Int. J. Hybrid Intell. Syst., vol. 20, no. 3, pp. 185–206, 2024. Crossref

[3] B. M. Aucoin and R. H. Jones, “High impedance fault detection implementation issues,” IEEE Trans. Power Del., vol. 11, no. 1, pp. 139–148, 1996. Crossref

[4] J. R. Camarillo-Peñaranda, J. Rodríguez-Reséndiz, and A. García-González, “High-impedance fault detection methods in power systems: A review,” Energies, vol. 14, no. 21, p. 7045, 2021. Crossref

[5] S. Kanwal and S. Jiriwibhakorn, “Advanced fault detection, classification, and localization in transmission lines: A comparative study of ANFIS, neural networks, and hybrid methods,” IEEE Access, vol. 12, pp. 49017–49033, 2024. Crossref

[6] S. Haykin, Neural Networks and Learning Machines, 3rd ed. Upper Saddle River, NJ, USA: Pearson, 2009.

[7] J.-S. R. Jang, “ANFIS: Adaptive-network-based fuzzy inference system,” IEEE Trans. Syst., Man, Cybern., vol. 23, no. 3, pp. 665–685, 1993. Crossref

[8] S. Jiriwibhakorn, “Critical clearing time prediction for power transmission using an adaptive neuro-fuzzy inference system,” IEEE Access, vol. 11, pp. 142100–142110, 2023. Crossref

[9] Z. S. Hussain, A. J. Ali, and A. A. Allu, “Improvement of protection relay with a single-phase autoreclosing mechanism based on artificial neural network,” Int. J. Power Electron. Drive Syst., vol. 11, no. 1, pp. 505–514, 2020. Crossref

[10] N. A.-B. Al-Jawady, M. A. Ibrahim, and L. A. Khalaf, “An intelligent overcurrent relay to protect transmission lines based on artificial neural network,” Int. J. Power Electron. Drive Syst., vol. 14, no. 2, pp. 1290–1299, 2023. Crossref

[11] Bhagwat, S. Dutta, and V. K. Jadoun, “A customised artificial neural network for power distribution system fault detection,” IET Gener. Transm. Distrib., vol. 18, no. 11, pp. 2105–2118, 2024. Crossref

[12] J. Chen, J. Gao, and Y. Jin, “Fault diagnosis in distributed power-generation systems using wavelet based artificial neural network,” Eur. J. Electr. Eng., vol. 23, no. 1, pp. 53–59, 2021. Crossref

[13] N. Kumar, P. Sridhar, and T. A. Kumar, “Adaptive neuro-fuzzy inference system based evolving fault locator for double circuit transmission lines,” IAES Int. J. Artif. Intell., vol. 9, no. 3, pp. 448–455, 2020. Crossref

[14] N. I. Okpura, E. N. C. Okafor, and K. M. Udofia, “Comparative analysis of fuzzy inference system (FIS) and adaptive neuro-fuzzy inference system (ANFIS) methods in the classification and location of high impedance faults on distribution system,” Eur. J. Eng. Technol. Res., vol. 5, no. 8, pp. 966–969, 2020.

[15] E. Nkan, E. E. Okpo, and O. I. Okoro, “Detection and classification of high impedance fault in Nigerian 330 kV transmission network using ANN: A case study of the Southeast transmission network,” ELEKTRIKA J. Electr. Eng., vol. 23, no. 1, pp. 65–74, 2024. Crossref

[16] E. Oduleye, I. E. Nkan, and E. E. Okpo, “High impedance fault detection and location in the Nigerian 330 kV transmission network using adaptive neuro-fuzzy inference system,” Int. J. Multidiscip. Res. Anal., vol. 6, no. 11, pp. 5390–5398, 2023. Crossref

[17] J. Atuchukwu, I. I. Okonkwo, A. W. Nwosu, and P. O. Anierobi, “Fault location on Nigeria's power transmission network using travelling wave technique: A MATLAB-based approach,” Global Sci. J., vol. 13, no. 6, pp. 316–329, 2025.

[18] A. M. Daang, A. M. Omas-as, and E. R. Arboleda, “Advancements in fault detection techniques for transmission lines: A literature review,” Int. J. Res. Publ. Rev., vol. 5, no. 7, pp. 553–572, 2024. Crossref

[19] IEEE Recommended Practice for Monitoring Electric Power Quality, IEEE Std. 1159-2019, 2019.

[20] E. H. Mamdani, “Advances in the linguistic synthesis of fuzzy controllers,” Int. J. Man-Mach. Stud., vol. 8, no. 6, pp. 669–678, 1976. Crossref

[21] Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016.

@article{1722893,
      author = {Nwoye Bernard Amobi, Ugochukwu Edebeani Anionovo, Abigail Chidimma Odigbo, Chika Obinna Ikaraoha, Charles Austeen Ibeh},
      title = {Comparative Analysis of Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System Techniques for Fault Detection and Mitigation on the Nigerian 330 kV Transmission Network},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {3},
      pages = {1227-1234},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1722893.pdf},
      abstract = {Transmission-line protection in modern power networks faces growing challenges from high-impedance faults, current-transformer saturation and power swings that degrade the performance of settings-based conventional distance relays. This paper reports a comparative simulation study of Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS)-derived intelligent protection techniques, benchmarked against Deep Learning (DL) and Genetic-Algorithm (GA) optimized variants, for fault detection, classification and mitigation on the 330 kV Onitsha–Enugu transmission corridor in Nigeria. A distributed-parameter, twenty-section π-model of the 250 km line, coupled with a ±150 MVAr Static Synchronous Compensator (STATCOM) under fuzzy-logic control, was developed in MATLAB/Simulink. Line-to-ground, line-to-line, double line-to-ground and three-phase faults were simulated at varying fault resistances, inception angles and locations to build a training and test dataset. A Multi-Layer Feed-Forward ANN trained with the Levenberg–Marquardt algorithm achieved 96.0% fault-classification accuracy with a fault-location error below 2.0%, while GA optimization raised classification accuracy to 97.2%. The fuzzy-logic-controlled STATCOM reduced voltage-recovery time from 0.25 s to 0.1 s (a 60% improvement) and limited the peak fault-current surge by 74%. The results confirm that ANN- and ANFIS-derived architectures markedly outperform conventional threshold-based protection and reveal an accuracy–interpretability trade-off that motivates hybrid intelligent-relay deployment.},
      keywords = {Adaptive neuro-fuzzy inference system (ANFIS); artificial neural network (ANN); fault classification; genetic algorithm; power-system protection; STATCOM; transmission line.},
      month = {September},
      doi = {https://doi.org/10.64388/IREV10I3-1722893}
  }