File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Intelligent Fault Detection Scheme for Microgrids with Wavelet-based Deep Neural Networks

TitleIntelligent Fault Detection Scheme for Microgrids with Wavelet-based Deep Neural Networks
Authors
Issue Date2018
PublisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5165411
Citation
IEEE Transactions on Smart Grid, 2018, p. 1-1 How to Cite?
AbstractFault detection is essential in microgrid control and operation, as it enables the system to perform fast fault isolation and recovery. The adoption of inverter-interfaced distributed generation in microgrids makes traditional fault detection schemes inappropriate due to their dependence on significant fault currents. In this paper, we devise an intelligent fault detection scheme for microgrid based on wavelet transform and deep neural networks. The proposed scheme aims to provide fast fault type, phase, and location information for microgrid protection and service recovery. In the scheme, branch current measurements sampled by protective relays are pre-processed by discrete wavelet transform to extract statistical features. Then all available data is input into deep neural networks to develop fault information. Compared with previous work, the proposed scheme can provide significantly better fault type classification accuracy. Moreover, the scheme can also detect the locations of faults, which are unavailable in previous work. To evaluate the performance of the proposed fault detection scheme, we conduct a comprehensive evaluation study on the CERTS microgrid and IEEE 34-bus system. The simulation results demonstrate the efficacy of the proposed scheme in terms of detection accuracy, computation time, and robustness against measurement uncertainty.
Persistent Identifierhttp://hdl.handle.net/10722/259310
ISSN
2015 Impact Factor: 3.19
2015 SCImago Journal Rankings: 4.784

 

DC FieldValueLanguage
dc.contributor.authorYu, JJ-
dc.contributor.authorHou, Y-
dc.contributor.authorLam, AYS-
dc.contributor.authorLi, VOK-
dc.date.accessioned2018-09-03T04:04:56Z-
dc.date.available2018-09-03T04:04:56Z-
dc.date.issued2018-
dc.identifier.citationIEEE Transactions on Smart Grid, 2018, p. 1-1-
dc.identifier.issn1949-3053-
dc.identifier.urihttp://hdl.handle.net/10722/259310-
dc.description.abstractFault detection is essential in microgrid control and operation, as it enables the system to perform fast fault isolation and recovery. The adoption of inverter-interfaced distributed generation in microgrids makes traditional fault detection schemes inappropriate due to their dependence on significant fault currents. In this paper, we devise an intelligent fault detection scheme for microgrid based on wavelet transform and deep neural networks. The proposed scheme aims to provide fast fault type, phase, and location information for microgrid protection and service recovery. In the scheme, branch current measurements sampled by protective relays are pre-processed by discrete wavelet transform to extract statistical features. Then all available data is input into deep neural networks to develop fault information. Compared with previous work, the proposed scheme can provide significantly better fault type classification accuracy. Moreover, the scheme can also detect the locations of faults, which are unavailable in previous work. To evaluate the performance of the proposed fault detection scheme, we conduct a comprehensive evaluation study on the CERTS microgrid and IEEE 34-bus system. The simulation results demonstrate the efficacy of the proposed scheme in terms of detection accuracy, computation time, and robustness against measurement uncertainty.-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5165411-
dc.relation.ispartofIEEE Transactions on Smart Grid-
dc.rightsIEEE Transactions on Smart Grid. Copyright © Institute of Electrical and Electronics Engineers.-
dc.rights©20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. -
dc.titleIntelligent Fault Detection Scheme for Microgrids with Wavelet-based Deep Neural Networks-
dc.typeArticle-
dc.identifier.emailYu, JJ: jqyu@eee.hku.hk-
dc.identifier.emailHou, Y: yhhou@hku.hk-
dc.identifier.emailLam, AYS: ayslam@eee.hku.hk-
dc.identifier.emailLi, VOK: vli@eee.hku.hk-
dc.identifier.authorityHou, Y=rp00069-
dc.identifier.authorityLam, AYS=rp02083-
dc.identifier.authorityLi, VOK=rp00150-
dc.identifier.doi10.1109/TSG.2017.2776310-
dc.identifier.hkuros289638-
dc.identifier.spage1-
dc.identifier.epage1-
dc.publisher.placeUnited States-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats