8c983b61-698a-4204-b19b-b2f59f246766 beie director@blueeyesintelligence.org 10.35940/ijrte.E6775.0110522 Electr Power Syst ResBaqui811325201110.1016/j.epsr.2011.01.022High impedance fault detection methodology using wavelet transform and artifcial neural networksBaqui I, Zamora I, Mazn J, Buigues G (2011) High impedance fault detection methodology using wavelet transform and artifcial neural networks. Electr Power Syst Res 81:1325-1333.IEEE/PES transmission and distribution conferenceBretas433200610.1109/tdcla.2006.311623A novel high impedance fault location for distribution systems considering distributed generationBretas AS, Moreto M, Salim RH, Pires LO (2006) A novel high impedance fault location for distribution systems considering distributed generation. In: IEEE/PES transmission and distribution conference, pp 433-439.10.1007/978-3-540-37275-2_74Bretas AS, Pires L, Moreto M, Salim RH, Huang D, Li K, Irwing G (2010) A bp neural network based technique for hif detection and location on distribution systems with distributed generation. Int Conf Intell Comput 4114:608-613.IEEE Trans Ind Appl 51(6)Costa5312201510.1109/TIA.2015.2434993Real-time detection of transients induced by high impedance faults based on the boundary wavelet transformCosta FB, Souza BA, Brito NS, Silva JAC (2015) Real-time detection of transients induced by high impedance faults based on the boundary wavelet transform. IEEE Trans Ind Appl 51(6):5312-5323.IET Gener Transm Distrib 2(1)Etemadi110200810.1049/iet-gtd:20070120High-impedance fault detection using multi-resolution signal decomposition and adaptive neural fuzzy inference systemEtemadi AH, Sanaye-Pasand M (2008) High-impedance fault detection using multi-resolution signal decomposition and adaptive neural fuzzy inference system. IET Gener Transm Distrib 2(1):110-118Electr Power Syst ResIbrahim80907201010.1016/j.epsr.2009.12.019Real time evaluation of DWT-based high impedance fault detection in EHV transmissionIbrahim DK, Eldin E, Aboul-Zahab E, Saleh S (2010) Real time evaluation of DWT-based high impedance fault detection in EHV transmission. Electr Power Syst Res 80:907-914.International joint conference on neural networks (IJCNN)Lucas1201810.1109/ijcnn.2018.8489453High impedance fault detection in time-varying distributed generation systems using adaptive neural networksLucas F, Costa P, Batalha R, Leite D (2018) High impedance fault detection in time-varying distributed generation systems using adaptive neural networks. In: International joint conference on neural networks (IJCNN), pp 1-8.IEEE Trans Power Deliv 32(1)Santos23201710.1109/TPWRD.2016.2548942High impedance fault identifcation on distribution networksSantos WC, Lopes FV, Brito NSD, Souza B (2017) High impedance fault identifcation on distribution networks. IEEE Trans Power Deliv 32(1):23-32Electr Power Syst ResSedighi76136200510.1016/j.epsr.2005.05.004Soft computing applications in high impedance fault detection in distribution systemsSedighi AR, Haghifam MR, Malik OP (2005) Soft computing applications in high impedance fault detection in distribution systems. Electr Power Syst Res 76:136-144.Electr Power Syst ResSilva154474201810.1016/j.epsr.2017.08.039High impedance fault detection in power distribution systems using wavelet transform and evolving neural networkSilva S, Costa P, Gouvea M, Lacerda A, Alves F, Leite D (2018) High impedance fault detection in power distribution systems using wavelet transform and evolving neural network. Electr Power Syst Res 154:474-483.Int J Electr Power 61(1)Torres163201410.1016/j.ijepes.2014.03.046Modeling and detection of high impedance faultsTorres V, Guardado JL, Ruiz HF, Maximov S (2014) Modeling and detection of high impedance faults. Int J Electr Power 61(1):163-172.Evolving SystemsLucas11165202010.1007/s12530-020-09328-3R. et al. Fault detection in smart grids with time-varying distributed generation using wavelet energy and evolving neural networksLucas, F., Costa, P., Batalha, R. et al. Fault detection in smart grids with time-varying distributed generation using wavelet energy and evolving neural networks. Evolving Systems 11, 165-180 (2020).IET Generation Transmission & DistributionEkici201910.1049/iet-gtd.2018.6213Comparison of Different Regression Models to Estimate Fault Location on Hybrid Power SystemsEkici, Sami & Ünal, Fatih & Ozleyen, Umit. (2019). Comparison of Different Regression Models to Estimate Fault Location on Hybrid Power Systems. IET Generation, Transmission & Distribution. 13. 10.1049/iet-gtd.2018.6213.1-6Bueno201910.1109/isgt-la.2019.8895475High Impedance Fault Detection and Classification in Medium Voltage Distribution NetworksBueno, Daniela & Santos, Eduardo & Stein, Henrique & Arian, Roya & Franco, Mateus & Rodrigues, Artur. (2019). High Impedance Fault Detection and Classification in Medium Voltage Distribution Networks. 1-6. 10.1109/ISGT-LA.2019.8895475.10.35833/MPCE.2019.000255S. Admasie, S. B. A. Bukhari, T. Gush, R. Haider and C. H. Kim, "Intelligent Islanding Detection of Multi-distributed Generation Using Artificial Neural Network Based on Intrinsic Mode Function Feature," in Journal of Modern Power Systems and Clean Energy, vol. 8, no. 3, pp. 511-520, May 2020, doi: 10.35833/MPCE.2019.000255.Generation Transmission & Distribution IETWei201010.1049/iet-gtd.2010.0104Waveform matching approach for fault diagnosis of a high-voltage transmission line employing harmony search algorithmWei, L. & Guo, W. & Wen, Fushuan & Ledwich, Gerard & Liao, Zhiwei & Xin, J.. (2010). Waveform matching approach for fault diagnosis of a high-voltage transmission line employing harmony search algorithm. Generation, Transmission & Distribution, IET. 4. 801 - 809. 10.1049/iet-gtd.2010.0104.10.1049/iet-smt.2018.5071Shukla, S.K.; Koley, E.; Ghosh, S. DC offset estimation-based fault detection in transmission line during power swing using ensemble of decision tree. IET Sci. Meas. Technol. 2019, 13, 212-22210.1016/j.epsr.2017.03.030Almeidaa, A.R.; Almeidaa, O.M.; Juniora, B.; Barretob, L.; Barros, A.K. ICA feature extraction for the location and classification of faults in high-voltage transmission lines. Electron. Power Syst. Res. 2017,10.1080/15325008.2014.986776Nabamita, R.; Kesab, B. Detection, Classification, and Estimation of Fault Location on an Overhead Transmission Line Using S-transform and Neural Network. Electr. Power Components Syst. 2015, 4, 461-472.10.1109/TIT.2006.871582Donoho, D.L. Compressed sensing. IEEE Trans. Inf. Theory 2006, 52, 1289-1306.10.1109/ICEVT.2018.8628460A. Zaeni, T. Kasnalestari and U. Khayam, "Application of Wavelet Transformation Symlet Type and Coiflet Type For Partial Discharge Signals Denoising," 2018 5th International Conference on Electric Vehicular Technology (ICEVT), Surakarta, Indonesia, 2018, pp. 78-82, doi: 10.1109/ICEVT.2018.8628460.10.1109/WI-IAT.2011.132S. X. Yang, Y. J. Tian and C. H. Zhang, "Rule Extraction from Support Vector Machines and Its Applications," 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, Lyon, France, 2011, pp. 221-224, doi: 10.1109/WI-IAT.2011.132W.H. Kersting, Radial distribution test feeders, IEEE Power Engineering Society Winter Meeting (2001) 908-912.10.1007/s11063-020-10270-3Sultana, Arifa, Aroop Bardalai, and Kandarpa Kumar Sarma. "Wireless sensor network based smart grid supported by a cognitively driven load management decision making." Neural Processing Letters 52 (2020): 663-678.10.1016/j.future.2017.09.051Yang, Xuan, Guangjie Han, Li Liu, Aihua Qian, and Wenbo Zhang. "IGRC: An improved grid-based joint routing and charging algorithm for wireless rechargeable sensor networks." Future Generation Computer Systems 92 (2019): 837-845.