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A detailed model is constructed to analyze fault patterns occurring on a dual feed system with multiple series compensation provided on EHV transmission line. The algorithm uses a full cycle of post-fault currents. A Multiresolution Analysis Wavelet-based decomposition technique is used to provide a joint time frequency analysis. Extensive study is done by designing different fault classifiers using Support Vector Machine (SVM) with different feature vector groups. The SVMs with different parameters are compared to show performance of SVMs in given feature space of faults. A new algorithm is proposed with Ensemble method having group of different classifiers, namely, Artificial Neural Network (ANN), K-Nearest Neighborhood (KNN) and SVM.\u00a0A combination of feature selection with Ensemble Classifiers is trained and tested on a wide range of fault patterns. A significant improvement in performance is obtained with different combination of features in Ensemble Classifier using subspace partition method.The proposed Ensemble Classifier provides an accuracy of 99.5%. <\/jats:p>","DOI":"10.1142\/s0218001420500093","type":"journal-article","created":{"date-parts":[[2019,5,16]],"date-time":"2019-05-16T02:33:31Z","timestamp":1557974011000},"page":"2050009","source":"Crossref","is-referenced-by-count":1,"title":["A Novel Fault Classification Technique in Series Compensated Transmission Line using Ensemble Method"],"prefix":"10.1142","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5001-7922","authenticated-orcid":false,"given":"Pranav D.","family":"Raval","sequence":"first","affiliation":[{"name":"Lukhdhirji Engineering College, Morbi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A.\u00a0S.","family":"Pandya","sequence":"additional","affiliation":[{"name":"AVPTI, Rajkot, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"219","published-online":{"date-parts":[[2019,8,1]]},"reference":[{"key":"S0218001420500093BIB001","doi-asserted-by":"publisher","DOI":"10.1142\/S0218001414600052"},{"key":"S0218001420500093BIB002","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2012.09.010"},{"key":"S0218001420500093BIB003","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-02326-2"},{"key":"S0218001420500093BIB004","doi-asserted-by":"publisher","DOI":"10.3906\/elk-1202-71"},{"key":"S0218001420500093BIB005","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCC.2011.2161285"},{"key":"S0218001420500093BIB006","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2006.123"},{"key":"S0218001420500093BIB007","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2010.06.010"},{"key":"S0218001420500093BIB008","doi-asserted-by":"publisher","DOI":"10.1109\/61.847231"},{"key":"S0218001420500093BIB009","volume-title":"Understanding Facts","author":"Hingorani N. 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