{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T16:08:08Z","timestamp":1782317288324,"version":"3.54.5"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"16","license":[{"start":{"date-parts":[[2022,4,21]],"date-time":"2022-04-21T00:00:00Z","timestamp":1650499200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,4,21]],"date-time":"2022-04-21T00:00:00Z","timestamp":1650499200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["RGPIN-2018-06222"],"award-info":[{"award-number":["RGPIN-2018-06222"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["RGPIN2017-04772"],"award-info":[{"award-number":["RGPIN2017-04772"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2022,8]]},"DOI":"10.1007\/s00521-022-07219-z","type":"journal-article","created":{"date-parts":[[2022,4,21]],"date-time":"2022-04-21T12:03:02Z","timestamp":1650542582000},"page":"14067-14084","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Deep learning for high-impedance fault detection and classification: transformer-CNN"],"prefix":"10.1007","volume":"34","author":[{"given":"Khushwant","family":"Rai","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Farnam","family":"Hojatpanah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Firouz Badrkhani","family":"Ajaei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Josep M.","family":"Guerrero","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0062-8212","authenticated-orcid":false,"given":"Katarina","family":"Grolinger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,4,21]]},"reference":[{"issue":"4","key":"7219_CR1","doi-asserted-by":"publisher","first-page":"3783","DOI":"10.1109\/TSG.2016.2642988","volume":"9","author":"B Wang","year":"2018","unstructured":"Wang B, Geng J, Dong X (2018) High-impedance fault detection based on nonlinear voltage-current characteristic profile identification. IEEE Trans Smart Grid 9(4):3783\u20133791","journal-title":"IEEE Trans Smart Grid"},{"issue":"2","key":"7219_CR2","doi-asserted-by":"publisher","first-page":"1226","DOI":"10.1109\/TPWRS.2012.2215630","volume":"28","author":"S Gautam","year":"2013","unstructured":"Gautam S, Brahma SM (2013) Detection of high impedance fault in power distribution systems using mathematical morphology. IEEE Trans Power Syst 28(2):1226\u20131234","journal-title":"IEEE Trans Power Syst"},{"issue":"3","key":"7219_CR3","doi-asserted-by":"publisher","first-page":"1603","DOI":"10.1109\/TPWRD.2020.3011930","volume":"36","author":"M Wei","year":"2021","unstructured":"Wei M, Liu W, Zhang H, Shi F, Chen W (2021) Distortion-based detection of high impedance fault in distribution systems. IEEE Trans Power Deliv 36(3):1603\u20131618","journal-title":"IEEE Trans Power Deliv"},{"issue":"4","key":"7219_CR4","doi-asserted-by":"publisher","first-page":"4365","DOI":"10.1109\/JSYST.2019.2942093","volume":"13","author":"H-G Yeh","year":"2019","unstructured":"Yeh H-G, Sim S, Bravo RJ (2019) Wavelet and denoising techniques for real-time HIF detection in 12-kv distribution circuits. IEEE Syst J 13(4):4365\u20134373","journal-title":"IEEE Syst J"},{"issue":"6","key":"7219_CR5","doi-asserted-by":"publisher","first-page":"7208","DOI":"10.1109\/TIA.2020.3017698","volume":"56","author":"S Wang","year":"2020","unstructured":"Wang S, Dehghanian P (2020) On the use of artificial intelligence for high impedance fault detection and electrical safety. IEEE Trans Ind Appl 56(6):7208\u20137216","journal-title":"IEEE Trans Ind Appl"},{"key":"7219_CR6","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1016\/j.epsr.2016.10.021","volume":"143","author":"A Ghaderi","year":"2017","unstructured":"Ghaderi A, Ginn HL III, Mohammadpour HA (2017) High impedance fault detection: a review. Electr Power Syst Res 143:376\u2013388","journal-title":"Electr Power Syst Res"},{"issue":"1","key":"7219_CR7","doi-asserted-by":"publisher","first-page":"797","DOI":"10.1109\/TSG.2019.2926668","volume":"11","author":"Q Cui","year":"2020","unstructured":"Cui Q, Weng Y (2020) Enhance high impedance fault detection and location accuracy via $$\\mu $$ -PMUs. IEEE Trans Smart Grid 11(1):797\u2013809","journal-title":"IEEE Trans Smart Grid"},{"issue":"4","key":"7219_CR8","doi-asserted-by":"publisher","first-page":"1806","DOI":"10.1109\/TPWRD.2015.2507541","volume":"31","author":"LU Iurinic","year":"2016","unstructured":"Iurinic LU, Herrera-Orozco AR, Ferraz RG, Bretas AS (2016) Distribution systems high-impedance fault location: a parameter estimation approach. IEEE Trans Power Deliv 31(4):1806\u20131814","journal-title":"IEEE Trans Power Deliv"},{"issue":"2","key":"7219_CR9","doi-asserted-by":"publisher","first-page":"557","DOI":"10.1109\/61.131112","volume":"6","author":"WH Kwon","year":"1991","unstructured":"Kwon WH, Lee GW, Park YM, Yoon MC, Yoo MH (1991) High impedance fault detection utilizing incremental variance of normalized even order harmonic power. IEEE Trans Power Deliv 6(2):557\u2013564","journal-title":"IEEE Trans Power Deliv"},{"issue":"2","key":"7219_CR10","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1109\/TPWRD.2003.820418","volume":"19","author":"Y Sheng","year":"2004","unstructured":"Sheng Y, Rovnyak SM (2004) Decision tree-based methodology for high impedance fault detection. IEEE Trans Power Deliv 19(2):533\u2013536","journal-title":"IEEE Trans Power Deliv"},{"issue":"4","key":"7219_CR11","doi-asserted-by":"publisher","first-page":"1714","DOI":"10.1109\/61.103666","volume":"5","author":"AA Girgis","year":"1990","unstructured":"Girgis AA, Chang W, Makram EB (1990) Analysis of high-impedance fault generated signals using a Kalman filtering approach. IEEE Trans Power Deliv 5(4):1714\u20131724","journal-title":"IEEE Trans Power Deliv"},{"key":"7219_CR12","doi-asserted-by":"crossref","unstructured":"Lima, \u00c9.M., dos Santos\u00a0Junqueira, C.M., Brito, N.S.D., de Souza, B.A., de Almeida\u00a0Coelho, R., de Medeiros, H.G.M.S.: High impedance fault detection method based on the short-time Fourier transform. IET Gener. Transm. Distrib. 12(11), 2577\u20132584 (2018)","DOI":"10.1049\/iet-gtd.2018.0093"},{"key":"7219_CR13","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1016\/j.ijepes.2015.05.010","volume":"73","author":"J-Y Cheng","year":"2015","unstructured":"Cheng J-Y, Huang S-J, Hsieh C-T (2015) Application of Gabor\u2013Wigner transform to inspect high-impedance fault-generated signals. Int J Electr Power Energy Syst 73:192\u2013199","journal-title":"Int J Electr Power Energy Syst"},{"issue":"3","key":"7219_CR14","doi-asserted-by":"publisher","first-page":"1260","DOI":"10.1109\/TPWRD.2014.2361207","volume":"30","author":"A Ghaderi","year":"2015","unstructured":"Ghaderi A, Mohammadpour HA, Ginn HL, Shin Y-J (2015) High-impedance fault detection in the distribution network using the time-frequency-based algorithm. IEEE Trans Power Deliv 30(3):1260\u20131268","journal-title":"IEEE Trans Power Deliv"},{"issue":"1","key":"7219_CR15","doi-asserted-by":"publisher","first-page":"870","DOI":"10.1109\/JSYST.2019.2911529","volume":"14","author":"BK Chaitanya","year":"2020","unstructured":"Chaitanya BK, Yadav A, Pazoki M (2020) An intelligent detection of high-impedance faults for distribution lines integrated with distributed generators. IEEE Syst J 14(1):870\u2013879","journal-title":"IEEE Syst J"},{"issue":"12","key":"7219_CR16","doi-asserted-by":"publisher","first-page":"9127","DOI":"10.1007\/s00521-019-04445-w","volume":"31","author":"V Veerasamy","year":"2019","unstructured":"Veerasamy V, Wahab NIA, Ramachandran R, Thirumeni M, Subramanian C, Othman ML, Hizam H (2019) High-impedance fault detection in medium-voltage distribution network using computational intelligence-based classifiers. Neural Comput Appl 31(12):9127\u20139143","journal-title":"Neural Comput Appl"},{"issue":"1","key":"7219_CR17","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1109\/TPWRD.2007.911146","volume":"23","author":"M Michalik","year":"2008","unstructured":"Michalik M, Lukowicz M, Rebizant W, Lee S-J, Kang S-H (2008) New ann-based algorithms for detecting HIFs in multigrounded MV networks. IEEE Trans Power Deliv 23(1):58\u201366","journal-title":"IEEE Trans Power Deliv"},{"issue":"7","key":"7219_CR18","doi-asserted-by":"publisher","first-page":"1325","DOI":"10.1016\/j.epsr.2011.01.022","volume":"81","author":"I Baqui","year":"2011","unstructured":"Baqui I, Zamora I, Maz\u00f3n J, Buigues G (2011) High impedance fault detection methodology using wavelet transform and artificial neural networks. Electr Power Syst Res 81(7):1325\u20131333","journal-title":"Electr Power Syst Res"},{"key":"7219_CR19","doi-asserted-by":"publisher","first-page":"116177","DOI":"10.1016\/j.apenergy.2020.116177","volume":"282","author":"MN Fekri","year":"2021","unstructured":"Fekri MN, Patel H, Grolinger K, Sharma V (2021) Deep learning for load forecasting with smart meter data: online adaptive recurrent neural network. Appl Energy 282:116177","journal-title":"Appl Energy"},{"key":"7219_CR20","doi-asserted-by":"publisher","first-page":"32672","DOI":"10.1109\/ACCESS.2021.3060800","volume":"9","author":"V Veerasamy","year":"2021","unstructured":"Veerasamy V, Wahab NIA, Othman ML, Padmanaban S, Sekar K, Ramachandran R, Hizam H, Vinayagam A, Islam MZ (2021) LSTM recurrent neural network classifier for high impedance fault detection in solar PV integrated power system. IEEE Access 9:32672\u201332687","journal-title":"IEEE Access"},{"issue":"3","key":"7219_CR21","doi-asserted-by":"publisher","first-page":"3465","DOI":"10.1109\/TSG.2018.2828414","volume":"10","author":"S Chakraborty","year":"2019","unstructured":"Chakraborty S, Das S (2019) Application of smart meters in high impedance fault detection on distribution systems. IEEE Trans Smart Grid 10(3):3465\u20133473","journal-title":"IEEE Trans Smart Grid"},{"issue":"5","key":"7219_CR22","doi-asserted-by":"publisher","first-page":"1325","DOI":"10.1049\/iet-gtd.2016.1657","volume":"11","author":"A Soheili","year":"2017","unstructured":"Soheili A, Sadeh J (2017) Evidential reasoning based approach to high impedance fault detection in power distribution systems. IET Gener Transm Distrib 11(5):1325\u20131336","journal-title":"IET Gener Transm Distrib"},{"key":"7219_CR23","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., Polosukhin, I.: Attention is all you need. In: Adv. Neural Inf. Process. Syst., pp. 5998\u20136008 (2017)"},{"key":"7219_CR24","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1016\/j.isprsjprs.2020.06.006","volume":"169","author":"M Ru\u00dfwurm","year":"2020","unstructured":"Ru\u00dfwurm M, K\u00f6rner M (2020) Self-attention for raw optical satellite time series classification. ISPRS J Photogramm Remote Sens 169:421\u2013435","journal-title":"ISPRS J Photogramm Remote Sens"},{"issue":"11","key":"7219_CR25","doi-asserted-by":"publisher","first-page":"7067","DOI":"10.1109\/TIE.2016.2582729","volume":"63","author":"T Ince","year":"2016","unstructured":"Ince T, Kiranyaz S, Eren L, Askar M, Gabbouj M (2016) Real-time motor fault detection by 1-d convolutional neural networks. IEEE Trans Ind Electron 63(11):7067\u20137075","journal-title":"IEEE Trans Ind Electron"},{"key":"7219_CR26","doi-asserted-by":"publisher","first-page":"133982","DOI":"10.1109\/ACCESS.2020.3010715","volume":"8","author":"D Gholamiangonabadi","year":"2020","unstructured":"Gholamiangonabadi D, Kiselov N, Grolinger K (2020) Deep neural networks for human activity recognition with wearable sensors: leave-one-subject-out cross-validation for model selection. IEEE Access 8:133982\u2013133994","journal-title":"IEEE Access"},{"key":"7219_CR27","doi-asserted-by":"crossref","unstructured":"Rai, K., Hojatpanah, F., Badrkhani\u00a0Ajaei, F., Grolinger, K.: Deep learning for high-impedance fault detection: convolutional autoencoders. Energies 14(12) (2021)","DOI":"10.3390\/en14123623"},{"issue":"3","key":"7219_CR28","doi-asserted-by":"publisher","first-page":"975","DOI":"10.1109\/59.119237","volume":"6","author":"WH Kersting","year":"1991","unstructured":"Kersting WH (1991) Radial distribution test feeders. IEEE Trans Power Syst 6(3):975\u2013985","journal-title":"IEEE Trans Power Syst"},{"issue":"2","key":"7219_CR29","doi-asserted-by":"publisher","first-page":"837","DOI":"10.1109\/TPWRD.2019.2929329","volume":"35","author":"M Wei","year":"2020","unstructured":"Wei M, Shi F, Zhang H, Jin Z, Terzija V, Zhou J, Bao H (2020) High impedance arc fault detection based on the harmonic randomness and waveform distortion in the distribution system. IEEE Trans Power Deliv 35(2):837\u2013850","journal-title":"IEEE Trans Power Deliv"},{"issue":"1","key":"7219_CR30","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1109\/TPWRD.2016.2548942","volume":"32","author":"W Santos","year":"2017","unstructured":"Santos W, Lopes F, Brito N, Souza B (2017) High-impedance fault identification on distribution networks. IEEE Trans Power Deliv 32(1):23\u201332","journal-title":"IEEE Trans Power Deliv"},{"issue":"3","key":"7219_CR31","doi-asserted-by":"publisher","first-page":"1203","DOI":"10.1109\/TPWRD.2019.2901634","volume":"34","author":"Q Cui","year":"2019","unstructured":"Cui Q, El-Arroudi K, Weng Y (2019) A feature selection method for high impedance fault detection. IEEE Trans Power Deliv 34(3):1203\u20131215","journal-title":"IEEE Trans Power Deliv"},{"issue":"1","key":"7219_CR32","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1109\/TPWRD.2004.837836","volume":"20","author":"TM Lai","year":"2005","unstructured":"Lai TM, Snider LA, Lo E, Sutanto D (2005) High-impedance fault detection using discrete wavelet transform and frequency range and RMS conversion. IEEE Trans Power Deliv 20(1):397\u2013407","journal-title":"IEEE Trans Power Deliv"},{"issue":"6","key":"7219_CR33","doi-asserted-by":"publisher","first-page":"3825","DOI":"10.1109\/TPWRD.2021.3049572","volume":"36","author":"M Biswal","year":"2021","unstructured":"Biswal M, Ghore S, Malik O, Bansal RC (2021) Development of time-frequency based approach to detect high impedance fault in an inverter interfaced distribution system. IEEE Trans Power Deliv 36(6):3825\u20133833","journal-title":"IEEE Trans Power Deliv"},{"key":"7219_CR34","doi-asserted-by":"crossref","unstructured":"Sokolova, M., Japkowicz, N., Szpakowicz, S.: Beyond accuracy, F-score and ROC: a family of discriminant measures for performance evaluation. In: Australasian joint conference on artificial intelligence, pp 1015\u20131021 (2006)","DOI":"10.1007\/11941439_114"},{"key":"7219_CR35","doi-asserted-by":"publisher","first-page":"1631","DOI":"10.1007\/s42835-020-00456-z","volume":"15","author":"N Narasimhulu","year":"2020","unstructured":"Narasimhulu N, Kumar DA, Kumar MV (2020) LWT based ANN with ant lion optimizer for detection and classification of high impedance faults in distribution system. J Electr Eng Technol 15:1631\u20131650","journal-title":"J Electr Eng Technol"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07219-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07219-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07219-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,24]],"date-time":"2022-07-24T10:16:31Z","timestamp":1658657791000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07219-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,21]]},"references-count":35,"journal-issue":{"issue":"16","published-print":{"date-parts":[[2022,8]]}},"alternative-id":["7219"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07219-z","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,21]]},"assertion":[{"value":"17 September 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 March 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 April 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that there is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest:"}}]}}