[1] Vidya Venkatesh (2018). Fault Classification and Location Identification on Electrical Transmission Network Based on Machine Learning Methods. 2022; Virginia Commonwealth University
[2] Stefanidou Voziki, Paschalia & Sapountzoglou, Nikolaos & Raison, Bertrand & Dominguez-Garcia, José Luis. (2022). A review of fault location and classification methods in distribution grids. Electric Power Systems Research. 209. 108031. 10.1016/j.epsr.2022.108031.
[3] De La Cruz, J., Gómez-Luna, E., Ali, M., Vasquez, J. C., & Guerrero, J. M. (2023). Fault Location for Distribution Smart Grids: Literature Overview, Challenges, Solutions, and Future Trends. Energies, 16(5), 2280. https://doi.org/10.3390/en16052280
[4] Alayande, Akintunde. S., Okakwu, I. K., Olabode, O. E. and Nwankwoh, O. K. (2021). Analysis of unsymmetrical faults based on artificial neural network using 11 kV distribution network of University of Lagos as case study. Journal of Advances in Science and Engineering. 4(1); 53-64
[5] Özdemir, Ö., Köker, R., & Pamuk, N. (2025). Fault Classification and Precise Fault Location Detection in 400 kV High-Voltage Power Transmission Lines Using Machine Learning Algorithms. Processes, 13(2), 527. https://doi.org/10.3390/pr13020527
[6] Almasoudi, F. M. (2023). Enhancing Power Grid Resilience through Real-Time Fault Detection and Remediation Using Advanced Hybrid Machine Learning Models. Sustainability, 15(10), 8348. https://doi.org/10.3390/su15108348
[7] Liang, Lingyu & Zhang, Huanming & Cao, Shang & Zhao, Xiangyu & Li, Hanju & Chen, Zhiwei. (2024). Fault location method for distribution networks based on multi-head graph attention networks. Frontiers in Energy Research. 12. 10.3389/fenrg.2024.1395737.
[8] D. Chanda and N. Y. Soltani, "Graph-Based Multi-Task Learning For Fault Detection In Smart Grid," 2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP), Rome, Italy, 2023, pp. 1-6, doi: 10.1109/MLSP55844.2023.10285865.
[9] Bang, N., Vu, T., Nguyen, T., Panwar, M., & Hovsapian, R. (2022). Spatial-Temporal Recurrent Graph Neural Networks for Fault Diagnostics in Power Distribution Systems. IEEE Access, 11, 46039-46050 frontiersin.org+4arxiv.org+4etasr.com+4sciencedirect.com
[10] Baskar. D. and Dr. Selvam. (2020). Machine Learning Framework for Power System Fault Detection and Classification. International Journal of Scientific & Technology Research. 9(2);277-8616.
[11] Rahman Dashti, Mohammad Daisy, Hamid Mirshekali, Hamid Reza Shaker, Mahmood Hosseini Aliabadi. (2021). A survey of fault prediction and location methods in electrical energy distribution networks. Measurement, 184, 109947. mdpi.com+3sciencedirect.com+3mdpi.com+3
[12] Mirshekali, H., Dashti, R., Keshavarz, A., & Shaker, H. R. (2022). Machine Learning-Based Fault Location for Smart Distribution Networks Equipped with Micro-PMU. Sensors, 22(3), 945. https://doi.org/10.3390/s22030945
[13] Thukaram (2004). Detection of fault in Distribution systems using artificial neural network. National power system conference (NPSC), IEEE, 9p
[14] Muhammad M.A.S. Mahmoud (2013). 3-Phase Fault Finding in Oil Field MV Distribution Network Using Fuzzy Clustering Techniques. Journal of Energy and Power Engineering, 7: 155-161.
[15] Avagaddi Prasad and J. Belwin Edward (2016). Application of Wavelet Technique for Fault Classification in Transmission Systems. School of Electrical Engineering, VIT University, Vellore, Tamil Nadu, 632014, India
[16] Q. Wu, C. Dong, F. Guo, L. Wang, X. Wu and C. Wen, "Privacy-Preserving Federated Learning for Power Transformer Fault Diagnosis With Unbalanced Data," in IEEE Transactions on Industrial Informatics, vol. 20, no. 4, pp. 5383-5394, April 2024, doi: 10.1109/TII.2023.3333914.
[17] Dana F. Doghramachi, Siddeeq Y. Ameen. (2023). Internet of Things (IoT) Security Enhancement Using XGboost Machine Learning Techniques. Computers, Materials and Continua, 77(1): 717-732.
[18] Djaballah, Said & Meftah, Kamel & Khelil, Khaled & Sayadi, Mounir. (2023). Deep Transfer Learning for Bearing Fault Diagnosis using CWT Time–Frequency Images and Convolutional Neural Networks. Journal of Failure Analysis and Prevention. 10.1007/s11668-023-01645-4.
[19] Liang, Lingyu & Zhang, Huanming & Cao, Shang & Zhao, Xiangyu & Li, Hanju & Chen, Zhiwei. (2024). Fault location method for distribution networks based on multi-head graph attention networks. Frontiers in Energy Research. 12. 10.3389/fenrg.2024.1395737.
[20] Ngo, Quang-Ha & Nguyen, Bang & Zhang, Jianhua & Schoder, Karl & Ginn, Herbert & Vu, Tuyen. (2024). Deep Graph Neural Network for Fault Detection and Identification in Distribution Systems. 10.36227/techrxiv.172555531.16904989/v1.
[21] B Thomas, Jibin & Chaudhari, Saurabh & Shihabudheen, K. & Verma, Nishchal. (2023). CNN-Based Transformer Model for Fault Detection in Power System Networks. IEEE Transactions on Instrumentation and Measurement. PP. 1-1. 10.1109/TIM.2023.3238059.
[22] Zhang, J. & He, Zhengyou & Lin, Sheng & Zhang, Y.B. & Qian, Q.Q.. (2013). An ANFIS-based fault classification approach in power distribution system. International Journal of Electrical Power & Energy Systems. 49. 243–252. 10.1016/j.ijepes.2012.12.005.
[23] Kanwal, S, and Jiriwibhakorn, S. (2024). Advanced Fault Detection, Classification, and Localization in Transmission Lines: A Comparative Study of ANFIS, Neural Networks, and Hybrid Methods. IEEE Access. 12. 49017-49033. 10.1109/ACCESS.2024.3384761.
[24] Goutam Kumar Yadav*, Mukesh Kumar Kirar, S.C. Gupta and Jatoth Rajender. Integrating ANN and ANFIS for effective fault detection and location in modern power grid.Science and Technology for Energy Transition 80, 34.
[25] Mohamed, M. A., Hassan, M. A. M., Albalawi, F., Ghoneim, S. S. M., Ali, Z. M., & Dardeer, M. (2021). Diagnostic Modelling for Induction Motor Faults via ANFIS Algorithm and DWT-Based Feature Extraction. Applied Sciences, 11(19), 9115. https://doi.org/10.3390/app11199115