{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:19:58Z","timestamp":1742912398769,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319479545"},{"type":"electronic","value":"9783319479552"}],"license":[{"start":{"date-parts":[[2016,1,1]],"date-time":"2016-01-01T00:00:00Z","timestamp":1451606400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016]]},"DOI":"10.1007\/978-3-319-47955-2_31","type":"book-chapter","created":{"date-parts":[[2016,10,13]],"date-time":"2016-10-13T15:04:10Z","timestamp":1476371050000},"page":"376-387","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A New Strategy Based on Feature Selection for Fault Classification in Transmission Lines"],"prefix":"10.1007","author":[{"given":"M\u00e1rcia","family":"Homci","sequence":"first","affiliation":[]},{"given":"Paulo","family":"Chagas","sequence":"additional","affiliation":[]},{"given":"Brunelli","family":"Miranda","sequence":"additional","affiliation":[]},{"given":"Jean","family":"Freire","sequence":"additional","affiliation":[]},{"suffix":"Jr.","given":"Raimundo","family":"Vi\u00e9gas","sequence":"additional","affiliation":[]},{"given":"Yomara","family":"Pires","sequence":"additional","affiliation":[]},{"given":"Bianchi","family":"Meiguins","sequence":"additional","affiliation":[]},{"given":"Jefferson","family":"Morais","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2016,10,14]]},"reference":[{"key":"31_CR1","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1016\/j.ijepes.2015.08.005","volume":"74","author":"H Fathabadi","year":"2016","unstructured":"Fathabadi, H.: Novel filter based ANN approach for short-circuit faults detection, classification and location in power transmission lines. Int. J. Electr. Power Energy Syst. 74, 374\u2013383 (2016)","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"31_CR2","first-page":"353","volume":"7","author":"K Hosseini","year":"2015","unstructured":"Hosseini, K.: Short circuit fault classification and location in transmission lines using a combination of wavelet transform and support vector machines. Int. J. Electr. Eng. Inf. 7, 353\u2013365 (2015)","journal-title":"Int. J. Electr. Eng. Inf."},{"key":"31_CR3","doi-asserted-by":"crossref","unstructured":"Singh, M., Panigrahi, B., Maheshwari, R.P.: Transmission line fault detection, classification. In: International Conference Emerging Trends in Electrical and Computer Technology (ICETECT), Tamil Nadu, pp. 15\u201322 (2011)","DOI":"10.1109\/ICETECT.2011.5760084"},{"issue":"4","key":"31_CR4","doi-asserted-by":"publisher","first-page":"2058","DOI":"10.1109\/TPWRD.2006.876659","volume":"21","author":"KM Silva","year":"2006","unstructured":"Silva, K.M., et al.: Fault detection and classification in transmission lines based on wavelet transform and ANN. IEEE Trans. Power Delivery 21(4), 2058\u20132063 (2006)","journal-title":"IEEE Trans. Power Delivery"},{"issue":"4","key":"31_CR5","doi-asserted-by":"publisher","first-page":"2083","DOI":"10.1109\/TPWRD.2010.2052932","volume":"25","author":"J Morais","year":"2010","unstructured":"Morais, J., Pires, Y., Cardoso Jr., C., Klautau, A.: A framework for evaluating automatic classification of underlying causes of disturbances and its application to short-circuit faults. IEEE Trans. Power Delivery 25(4), 2083\u20132094 (2010)","journal-title":"IEEE Trans. Power Delivery"},{"issue":"4","key":"31_CR6","first-page":"1112","volume":"24","author":"M Gowrishankar","year":"2016","unstructured":"Gowrishankar, M., Nagaveni, P., Balakrishnan, P.: Transmission line fault detection and classification using discrete wavelet transform and artificial neural network. Middle-East J. Sci. Res. 24(4), 1112\u20131121 (2016)","journal-title":"Middle-East J. Sci. Res."},{"issue":"1","key":"31_CR7","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1109\/TSG.2013.2260421","volume":"5","author":"H Livani","year":"2013","unstructured":"Livani, H., Evrenosoglu, Y.: A machine learning and wavelet-based fault location method for hybrid transmission lines. IEEE Trans. Smart Grid 5(1), 51\u201359 (2013)","journal-title":"IEEE Trans. Smart Grid"},{"issue":"3","key":"31_CR8","first-page":"69","volume":"36","author":"M Nayeripour","year":"2015","unstructured":"Nayeripour, M., et al.: Fault detection and classification in transmission lines based on a combination of wavelet singular values and fuzzy logic. Cumhuriyet Sci. J. 36(3), 69\u201382 (2015)","journal-title":"Cumhuriyet Sci. J."},{"key":"31_CR9","doi-asserted-by":"crossref","unstructured":"Cardoso, C., et al.: Hierarchical Agglomerative clustering of short-circuit faults in transmission lines. In: 10th Brazilian Symposium on Neural Networks, Salvador, Brazil, pp. 87\u201392 (2008)","DOI":"10.1109\/SBRN.2008.24"},{"key":"31_CR10","doi-asserted-by":"crossref","unstructured":"Das, D., Singh, N.K., Sinha, A.K.: A comparison of Fourier transform, wavelet transform methods for detection, classification of faults on transmission lines. In: IEEE Power India Conference (2006)","DOI":"10.1109\/POWERI.2006.1632580"},{"issue":"3\u20134","key":"31_CR11","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1016\/j.epsr.2006.03.015","volume":"77","author":"N Zhang","year":"2007","unstructured":"Zhang, N., Kezunovic, M.: A real time fault analysis tool for monitoring operation of transmission line protective relay. Electr. Power Syst. Res. 77(3\u20134), 361\u2013370 (2007)","journal-title":"Electr. Power Syst. Res."},{"key":"31_CR12","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4419-1428-6_3752","volume-title":"Data Mining: Concepts and Techniques","author":"J Han","year":"2012","unstructured":"Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques. Morgan Kaufmann, Burlington (2012)"},{"key":"31_CR13","unstructured":"Li, M., Sleep, R.: A robust approach to sequence classification. In: International Conference on Tools with Artificial Intelligence (2005)"},{"issue":"3","key":"31_CR14","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1049\/iet-est.2014.0042","volume":"5","author":"R Telford","year":"2015","unstructured":"Telford, R., Galloway, S.: Fault classification and diagnostic system for unmanned aerial vehicle electrical networks based on hidden Markov models. IET Electr. Syst. Transp. 5(3), 103\u2013111 (2015)","journal-title":"IET Electr. Syst. Transp."},{"key":"31_CR15","doi-asserted-by":"crossref","unstructured":"Petitjean, F., et al.: Dynamic Time Warping averaging of time series allows faster, more accurate classification. In: 2014 IEEE International Conference on Data Mining, Shenzhen (2014)","DOI":"10.1109\/ICDM.2014.27"},{"key":"31_CR16","doi-asserted-by":"crossref","DOI":"10.1117\/3.633187","volume-title":"Artificial Neural Networks: An Introduction","author":"Kevin L. Priddy","year":"2005","unstructured":"Priddy, K.L., Keller, P.E.: The curse of dimensionality. In: Artificial Neural Networks: an Introduction, pp. 26\u201330. SPIE - The International Society for Optical Engineering, Washington (2005)"},{"issue":"1","key":"31_CR17","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.compeleceng.2013.11.024","volume":"40","author":"G Chandrashekar","year":"2014","unstructured":"Chandrashekar, G., Sahin, F.: A survey on feature selection methods. Comput. Electr. Eng. 40(1), 16\u201328 (2014)","journal-title":"Comput. Electr. Eng."},{"key":"31_CR18","unstructured":"UFPAFaults. http:\/\/www.laps.ufpa.br\/freedatasets\/UfpaFaults"},{"key":"31_CR19","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1007\/978-3-642-31537-4_13","volume-title":"Machine Learning and Data Mining in Pattern Recognition","author":"TM Oshiro","year":"2012","unstructured":"Oshiro, T.M., Perez, P.S., Baranauskas, J.A.: How many trees in a random forest? In: Perner, P. (ed.) MLDM 2012. LNCS (LNAI), vol. 7376, pp. 154\u2013168. Springer, Heidelberg (2012). doi:10.1007\/978-3-642-31537-4_13"},{"issue":"2","key":"31_CR20","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1023\/A:1009715923555","volume":"2","author":"CJC Burges","year":"1998","unstructured":"Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Disc. 2(2), 121\u2013167 (1998)","journal-title":"Data Min. Knowl. Disc."},{"key":"31_CR21","unstructured":"Reddy, M.J.B., Mohanta, D.K.: Detection, classification, localization of power system impulsive transients using S-transform. In: 9th International Conference on Environment and Electrical Engineering (EEEIC), Prague, Czech Republic (2010)"}],"container-title":["Lecture Notes in Computer Science","Advances in Artificial Intelligence - IBERAMIA 2016"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-47955-2_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T16:59:40Z","timestamp":1710262780000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-319-47955-2_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016]]},"ISBN":["9783319479545","9783319479552"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-47955-2_31","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2016]]},"assertion":[{"value":"14 October 2016","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IBERAMIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ibero-American Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"San Jos\u00e9","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Costa Rica","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2016","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2016","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 November 2016","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iberamia2016","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}