[1]
A. Raza, A. Benrabah, T. Alquthami, and M. Akmal, "A review of fault diagnosing methods in power transmission systems," Applied Sciences, vol. 10, no. 4, p.1312, 2020.
DOI: 10.3390/app10041312
Google Scholar
[2]
U. Shahzad and S. Asgarpoor, "A comprehensive review of protection schemes for distributed generation," Energy and Power Engineering, vol. 9, no. 08, p.430, 2017.
DOI: 10.4236/epe.2017.98029
Google Scholar
[3]
R. H. AlKuwaiti, W. T. El-Sayed, H. E. Farag, A. Al-Durra, and E. F. El-Saadany, "Power system resilience against climatic faults: An optimized self-healing approach using conservative voltage reduction," International Journal of Electrical Power & Energy Systems, vol. 155, p.109519, 2024.
DOI: 10.1016/j.ijepes.2023.109519
Google Scholar
[4]
A. S. Alayande, I. K. Okakwu, O. E. Olabode, and O. K. Nwankwoh, "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, vol. 4, no. 1, pp.53-64, 2021.
DOI: 10.37121/jase.v4i1.91
Google Scholar
[5]
I. Srivastava, S. Bhat, and A. R. Singh, "Fault diagnosis, service restoration, and data loss mitigation through multi-agent system in a smart power distribution grid," Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, pp.1-26, 2020.
DOI: 10.1080/15567036.2020.1817190
Google Scholar
[6]
S. Sarangi, B. K. Sahu, and P. K. Rout, "A comprehensive review of distribution generation integrated DC microgrid protection: issues, strategies, and future direction," International Journal of Energy Research, vol. 45, no. 4, pp.5006-5031, 2021.
DOI: 10.1002/er.6245
Google Scholar
[7]
R. Pérez, M. Rivera, Y. Salgueiro, C. R. Baier, and P. Wheeler, "Moving microgrid hierarchical control to an SDN-based kubernetes cluster: a framework for reliable and flexible energy distribution," Sensors, vol. 23, no. 7, p.3395, 2023.
DOI: 10.3390/s23073395
Google Scholar
[8]
R. K. Mathew, A. Sankar, and K. Sundaramoorthy, "An efficient and fast fault recovery algorithm for a resilient grid under extreme events," Energy sources, part A: recovery, utilization, and environmental effects, vol. 44, no. 2, pp.2914-2925, 2022.
DOI: 10.1080/15567036.2019.1652371
Google Scholar
[9]
Y. Zeng, C. Qin, J. Liu, and X. Xu, "Coordinating multiple resources for enhancing distribution system resilience against extreme weather events considering multi-stage coupling," International Journal of Electrical Power & Energy Systems, vol. 138, p.107901, 2022.
DOI: 10.1016/j.ijepes.2021.107901
Google Scholar
[10]
A. Samimi and M. Nikzad, "Optimal restoration of active distribution systems for enhancing resilience considering the uncertainty of renewable sources," Iranian Electric Industry Journal of Quality and Productivity, vol. 10, no. 3, pp.97-108, 2021.
Google Scholar
[11]
M. Aryanfar, G. Rahmani-Sane, M. H. Masali, and A. Mosallanejad, "Application of recovery techniques to enhance the resilience of power systems," in Future Modern Distribution Networks Resilience: Elsevier, 2024, pp.195-213.
DOI: 10.1016/b978-0-443-16086-8.00001-4
Google Scholar
[12]
M. G. Taul, X. Wang, P. Davari, and F. Blaabjerg, "Current limiting control with enhanced dynamics of grid-forming converters during fault conditions," IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 8, no. 2, pp.1062-1073, 2019.
DOI: 10.1109/jestpe.2019.2931477
Google Scholar
[13]
Fathabadi H (2016) Novel filter based ann approach for short-circuit faults detection, classification and location in power transmission lines. Int J Electric Power Energy Syst 74:374–383.
DOI: 10.1016/j.ijepes.2015.08.005
Google Scholar
[14]
Ferreira VH, Zanghi R, Fortes MZ, Gomes S Jr, da Silva APA (2020) Probabilistic transmission line fault diagnosis using autonomous neural models. Electric Power Syst Res 185:106360.
DOI: 10.1016/j.epsr.2020.106360
Google Scholar
[15]
Farshad M, Sadeh J (2012) Accurate single-phase fault-location method for transmission lines based on k-nearest neighbor algorithm using one-end voltage. IEEE Trans Power Delivery 27(4):2360–2367.
DOI: 10.1109/tpwrd.2012.2211898
Google Scholar
[16]
Fuada S, Shiddieqy HA, Adiono T (2020) A high-accuracy of transmission line faults (tlfs) classification based on convolutional neural network. IntJ Electron Telecommun 66(4):655–664.
DOI: 10.24425/ijet.2020.134024
Google Scholar
[17]
Godse R, Bhat S (2020) Mathematical morphology-based feature-extraction technique for detection and classification of faults on power transmission line. IEEE Access 8:38459–38471.
DOI: 10.1109/access.2020.2975431
Google Scholar
[18]
Chen YQ, Fink O, Sansavini G (2017) Combined fault location and classification for power transmission lines fault diagnosis with integrated feature extraction. IEEE Trans Industr Electron 65(1):561–569.
DOI: 10.1109/tie.2017.2721922
Google Scholar
[19]
Bingzhen Z, Xiaoming Q, Hemeng Y, Zhubo Z (2020) A random forest classification model for transmission line image processing. In: 2020 15th international conference on computer science & education (ICCSE). IEEE, p.613–617.
DOI: 10.1109/iccse49874.2020.9201900
Google Scholar
[20]
Bala P, Dalai S (2017) Random forest based fault analysis method in IEEE 14 bus system. In: 2017 3rd International conference on condition assessment techniques in electrical systems (CATCON). IEEE, p.407–411.
DOI: 10.1109/catcon.2017.8280254
Google Scholar