Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/153573
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dc.contributor.authorDeng, Yapingen_US
dc.contributor.authorLiu, Xinghuaen_US
dc.contributor.authorJia, Rongen_US
dc.contributor.authorHuang, Qien_US
dc.contributor.authorXiao, Gaoxien_US
dc.contributor.authorWang, Pengen_US
dc.date.accessioned2021-12-08T01:40:04Z-
dc.date.available2021-12-08T01:40:04Z-
dc.date.issued2021-
dc.identifier.citationDeng, Y., Liu, X., Jia, R., Huang, Q., Xiao, G. & Wang, P. (2021). Sag source location and type recognition via attention-based independently recurrent neural network. Journal of Modern Power Systems and Clean Energy, 9(5), 1018-1031. https://dx.doi.org/10.35833/MPCE.2020.000528en_US
dc.identifier.issn2196-5625en_US
dc.identifier.urihttps://hdl.handle.net/10356/153573-
dc.description.abstractAccurate sag source location and precise sag type recognition are both essential to verifying the responsible party for the sag and taking countermeasures to improve power quality. In this paper, an attention-based independently recurrent neural network (IndRNN) for sag source location and sag type recognition in sparsely monitored power system is proposed. Specially, the given inputs are voltage waveforms collected by limited meters in sparsely monitored power system, and the desired outputs simultaneously contain the following information: the located lines where sag occurs; the corresponding sag types, including motor starting, transformer energizing and short circuit; and the fault phase for short circuit. In essence, the responsibility of the proposed method is to automatically establish a nonlinear function that relates the given inputs to the desired outputs with categorization labels as few as possible. A favorable feature of the proposed method is that it can be realized without system parameters or models. The proposed method is validated by IEEE 30-bus system and a real 134-bus system. Experimental results demonstrate that the accuracy of sag source location is higher than 99% for all lines, and the accuracy of sag type recognition is also higher than 99% for various sag sources including motor starting, transformer energizing and 7 different types of short circuits. Furthermore, a comparison among different monitor placements for the proposed method is conducted, which illustrates that the observability of power networks should be ensured to achieve satisfactory performance.en_US
dc.language.isoenen_US
dc.relation.ispartofJournal of Modern Power Systems and Clean Energyen_US
dc.rights© 2021 IEEE. This journal is 100% open access, which means that all content is freely available without charge to users or their institutions. All articles accepted after 12 June 2019 are published under a CC BY 4.0 license, and the author retains copyright. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, as long as proper attribution is given.en_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleSag source location and type recognition via attention-based independently recurrent neural networken_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.identifier.doi10.35833/MPCE.2020.000528-
dc.description.versionPublished versionen_US
dc.identifier.scopus2-s2.0-85116057756-
dc.identifier.issue5en_US
dc.identifier.volume9en_US
dc.identifier.spage1018en_US
dc.identifier.epage1031en_US
dc.subject.keywordsIndependently Recurrent Neural Networken_US
dc.subject.keywordsSag Source Locationen_US
dc.description.acknowledgementThis work was partly supported by National Natural Science Foundation of China (No. 61903296), Key Project of Natural Science Basic Research Plan in Shaanxi Province of China (No. 2019ZDLGY18-03), Thousand Talents Plan of Shaanxi Province for Young Professionals, Project of Shaanxi Science and Tech‐ nology (No. 2019JQ-329), and Doctoral Scientific Research Foundation of Xi’an University of Technology (No. 103-451116012).en_US
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