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  2. School of Engineering
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  4. Publikationen School of Engineering
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dc.contributor.authorChen, Yann Qi-
dc.contributor.authorFink, Olga-
dc.contributor.authorSansavini, Giovanni-
dc.date.accessioned2018-12-18T15:38:23Z-
dc.date.available2018-12-18T15:38:23Z-
dc.date.issued2018-
dc.identifier.issn0278-0046de_CH
dc.identifier.issn1557-9948de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/13951-
dc.description.abstractAccurate and timely diagnosis of transmission line faults is key for reliable operations of power systems. Existing fault-diagnosis methods rely on expert knowledge or extensive feature extraction, which is also highly dependent on expert knowledge. Additionally, most methods for fault diagnosis of transmission lines require multiple separate subalgorithms for fault classification and location performing each function independently and sequentially. In this research, an integrated framework combining fault classification and location is proposed by applying an innovative machine-learning algorithm: the summation-wavelet extreme learning machine (SW-ELM) that integrates feature extraction in the learning process. As a further contribution, an extension of the SW-ELM, i.e., the summation-Gaussian extreme learning machine (SG-ELM), is proposed and successfully applied to transmission line fault diagnosis. SG-ELM is fully self-learning and does not require ad-hoc feature extraction, making it deployable with minimum expert subjectivity. The developed framework is applied to three transmission-line topologies without any prior parameter tuning or ad-hoc feature extraction. Evaluations on a simulated dataset show that the proposed method can diagnose faults within a single cycle, remain immune to fault resistance and inception angle variation, and deliver high accuracy for both tasks of fault diagnosis: fault type classification and fault location estimation.de_CH
dc.language.isoende_CH
dc.publisherIEEEde_CH
dc.relation.ispartofIEEE Transactions on Industrial Electronicsde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subject.ddc004: Informatikde_CH
dc.subject.ddc621.3: Elektro-, Kommunikations-, Steuerungs- und Regelungstechnikde_CH
dc.titleCombined fault location and classification for power transmission lines fault diagnosis with integrated feature extractionde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Datenanalyse und Prozessdesign (IDP)de_CH
dc.identifier.doi10.1109/TIE.2017.2721922de_CH
zhaw.funding.euNode_CH
zhaw.issue1de_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end569de_CH
zhaw.pages.start561de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume65de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
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Chen, Y. Q., Fink, O., & Sansavini, G. (2018). Combined fault location and classification for power transmission lines fault diagnosis with integrated feature extraction. IEEE Transactions on Industrial Electronics, 65(1), 561–569. https://doi.org/10.1109/TIE.2017.2721922
Chen, Y.Q., Fink, O. and Sansavini, G. (2018) ‘Combined fault location and classification for power transmission lines fault diagnosis with integrated feature extraction’, IEEE Transactions on Industrial Electronics, 65(1), pp. 561–569. Available at: https://doi.org/10.1109/TIE.2017.2721922.
Y. Q. Chen, O. Fink, and G. Sansavini, “Combined fault location and classification for power transmission lines fault diagnosis with integrated feature extraction,” IEEE Transactions on Industrial Electronics, vol. 65, no. 1, pp. 561–569, 2018, doi: 10.1109/TIE.2017.2721922.
CHEN, Yann Qi, Olga FINK und Giovanni SANSAVINI, 2018. Combined fault location and classification for power transmission lines fault diagnosis with integrated feature extraction. IEEE Transactions on Industrial Electronics. 2018. Bd. 65, Nr. 1, S. 561–569. DOI 10.1109/TIE.2017.2721922
Chen, Yann Qi, Olga Fink, and Giovanni Sansavini. 2018. “Combined Fault Location and Classification for Power Transmission Lines Fault Diagnosis with Integrated Feature Extraction.” IEEE Transactions on Industrial Electronics 65 (1): 561–69. https://doi.org/10.1109/TIE.2017.2721922.
Chen, Yann Qi, et al. “Combined Fault Location and Classification for Power Transmission Lines Fault Diagnosis with Integrated Feature Extraction.” IEEE Transactions on Industrial Electronics, vol. 65, no. 1, 2018, pp. 561–69, https://doi.org/10.1109/TIE.2017.2721922.


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