• [1] Aucoin, B.M. and Russell, B.D., "Distribution high impedance fault detection utilizing high frequency current components", IEEE Trans. Power Appar. Syst., PER-2, No. 6, pp. 46 - 47, 1982.
• [2] Mamishev, A.V., Russell, B.D. and Benner, C.L., "Analysis of high impedance faults using fractal techniques", IEEE Trans. Power Syst., Vol. 11, No. 1, pp. 435-440, 1996.
• [3] Benner, C.L. and Russell, B.D., "Practical high-impedance fault detection on distribution feeders", IEEE Trans. Ind. Appl. , Vol. 33, No. 3, pp. 635-640, 1997.
• [4] Gautam, S. and Brahma, S.M., "Detection of high impedance fault in power distribution systems using mathematical morphology",IEEE Trans. Power Syst., 28, No. 2, pp. 1226 - 1234, 2013.
• [5] Lazkano, A., Ruiz, J., Aramendi, E., Leturiondo, L.A. and Gonzalez, J.A., "Study of high impedance fault detection in Levante area in Spain", Ninth International Conference. Harmonics. Quality. Power. Proceedings (Cat. No. 00EX441), pp. 1011-1016, 2002.
• [6] Ghaderi, A., Ginn III, H.L. and Mohammadpour, H.A., "High impedance fault detection: A review", Electr. Power Syst. Res., Vol. 143, pp. 376-388, 2017.
• [7] Aucoin, B.M., and Jones, R.H., "High impedance fault detection implementation issues", IEEE Trans. Power Delivery., Vol. 11, No. 1, pp. 139 - 148, 1996.
• [8] Sarlak, M. and Shahrtash, S.M., "High impedance fault detection using combination of multi-layer perceptron neural networks based on multi-resolution morphological gradient features of current waveform", IET. Gener. Transm. Dis., 5, No. 5, pp. 588 - 595, 2011.
• [9] Sekar, K. and Mohanty, N.K., "A fuzzy rule base approach for High Impedance Fault detection in distribution system using Morphology Gradient filter", J. King Saud Univ. Eng. Sci., Vol. 32, No. 3, pp. 177-185, 2020.
• Sekar, K. and Mohanty, N.K., "Data mining-based high impedance fault detection using mathematical morphology", Electr. Eng., Vol. 69, pp. 129-141, 2018.
• Silva, S., Costa, P., Santana, M. and Leite, D., "Evolving neuro fuzzy network for real-time high impedance fault detection and classification", Neural Comput. Appl., 32, No. 12, pp. 7597–7610, 2020.
• Aziz, M.A., Hassan, M.M. and Zahab, E.A., "High-impedance faults analysis in distribution networks using an adaptive neuro fuzzy inference system", Power. Compon. Syst., Vol. 40, No. 11, pp.  1300-1318, 2012.
• Sahoo, S. and Baran, M.E., "A method to detect high impedance faults in distribution feeders", IEEE PES. Trans. Dis. Conf. Exposition., pp. 1-6, 2014.
• Moravej, Z., Mortazavi, S.H. and Shahrtash, S.M., "DT‐CWT based event feature extraction for high impedance faults detection in distribution system", Int Trans. Electr. Energ. Syst., Vol. 25, No. 12, pp. 3288-3303, 2015.
• Mortazavi, S.H., Moravej, Z. and Shahrtash, S.M.,"A hybrid method for arcing faults detection in large distribution networks", J. Electr. Power Energ Syst., Vol. 94, pp. 141-150, 2018.
• Zhang, S., Xiao, X. and He, Z., "Detection of highimpedance fault in distribution network based on time–frequency entropy of wavelet transform", IEEJ Trans. Electr. Electron. Eng., Vol. 15, No. 6, pp. 844-853, 2020.
• Mishra, M., Routray, P. and kumar Rout, P., "A universal high impedance fault detection technique for distribution system using S-transform and pattern recognition", Econ. Smart Grids. Sustain Energ, Vol. 1, No. 9, 2016.
• AsghariGovar, S., Pourghasem, P. and Seyedi, H., "High impedance fault protection scheme for smart grids based on WPT and ELM considering evolving and cross-country faults", j. Electr. Power energy syst., Vol. 107, pp. 412-421, 2019.
• Mohammadnian, Y., Amraee, T. and Soroudi, A., "Fault detection in distribution networks in presence of distributed generations using a data mining–driven wavelet transform", IET Smart Grid., Vol. 2, No. 2, pp. 163-171, 2019.
• Gadanayak, D.A. and Mallick, R.K., "Interharmonics based high impedance fault detection in distribution systems using maximum overlap wavelet packet transform and a modified empirical mode decomposition", J. Electr. Power Energy Syst., Vol. 112, pp.  282-293, 2019.
• Torres-Garcia, V., Guillen, D., Olveres, J., Escalante-Ramirez, B. and Rodriguez-Rodriguez, J.R., "Modelling of high impedance faults in distribution systems and validation based on multiresolution techniques", Comput. Electr Eng., Vol. 83, 2020.
• Silva, S., Costa, P., Gouvea, M., Lacerda, A., Alves, F. and Leite, D., "High impedance fault detection in power distribution systems using wavelet transform and evolving neural network", Electr. Power Syst., 154, pp.  474-483, 2018.
• Soheili, A., Sadeh, J. and Bakhshi, R., "Modified FFT based high impedance fault detection technique considering distribution non-linear loads: Simulation and experimental data analysis", Int. J. Electr. Power Energ Syst., Vol. 94, pp.  124-140, 2018.
• Gautam, S. and Brahma, S.M., "Detection of high impedance fault in power distribution systems using mathematical morphology", IEEE Trans. Power Syst., 28, No. 2, pp. 1226 - 1234, 2013.
• Ghaderi, A., Mohammadpour, H.A., Ginn, H.L. and Shin, Y.J., "High-impedance fault detection in the distribution network using the time-frequency-based algorithm", IEEE Trans. Power Delivery., 30, No. 3, pp. 1260 - 1268, 2015.
• Sarwar, M., Mehmood, F., Abid, M., Khan, A.Q., Gul, S.T. and Khan, A.S., "High impedance fault detection and isolation in power distribution networks using support vector machines", J. King Saud Univ. Eng. Sci., 2019.
• Cui, Q. and Weng, Y., "Enhance High Impedance Fault Detection and Location Accuracy via $\mu $-PMUs", IEEE Trans. Smart Grid., Vol. 11, No. 1, pp. 797 - 809, 2020.
• Yoon, W.K. and Devaney, M.J., "Reactive power measurement using the wavelet transform", IEEE Trans. Instrum. Meas., Vol. 49, No. 2, pp. 246 - 252, 2000.
• Morsi, W.G. and El-Hawary, M.E., "A new perspective for the IEEE standard 1459-2000 via stationary wavelet transform in the presence of nonstationary power quality disturbance", IEEE Trans. Power Delivery., Vol. 23, No. 4, pp. 2356 - 2365, 2008.
• Adewole, A.C., Tzoneva, R., and Behardien, S., "Distribution network fault section identification and fault location using wavelet entropy and neural networks", Applied soft computing, Vol. 46, pp. 296-306, 2016.

Distribution Test Feeders, IEEE PES Distribution System Analysis Subcommittee's, Distribution Test Feeder Working Group, August 2013. http://ewh.ieee.org/soc/pes/dsacom/testfeeders/feeder34.

• Japkowicz, N. and Shah, M., Evaluating learning algorithms: A classification perspective, Cambridge University Press, 2011.