Application of extreme learning machine for series compensated transmission line protection
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| Title: | Application of extreme learning machine for series compensated transmission line protection |
|---|---|
| Authors: | Malathi, V.1 vmeee@autmdu.ac.in, Marimuthu, N.S.2 sbeee@tce.edu, Baskar, S.3, Ramar, K.4 ramar@mmu.edu.my |
| Source: | Engineering Applications of Artificial Intelligence. Aug2011, Vol. 24 Issue 5, p880-887. 8p. |
| Subjects: | Machine learning, Electric lines testing, Wavelets (Mathematics), Electric fault location, Electric currents, Capacitors, Robust control, Feasibility studies |
| Abstract: | Abstract: This paper proposes a new approach based on combined Wavelet Transform-Extreme Learning Machine (WT-ELM) technique for fault section identification (whether the fault is before or after the series capacitor as observed from the relay point), classification and location in a series compensated transmission line. This method uses the samples of fault currents for half cycle duration from the inception of fault. The features of fault currents are extracted by first level decomposition of the current samples using discrete wavelet transform (DWT) and the extracted features are applied as inputs to ELMs for fault section identification, classification and location. The feasibility of the proposed method has been tested on a 400kV, 300km series compensated transmission line for all the ten types of faults using MATLAB simulink. On testing 28,800 fault cases with varying fault resistance, fault inception angle, fault distance, load angle, percentage compensation level and source impedance, the performance of the proposed method has been found to be quite promising. The results also indicate that the proposed method is robust to wide variation in system and operating conditions. [Copyright &y& Elsevier] |
| Copyright of Engineering Applications of Artificial Intelligence is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Items | – Name: Title Label: Title Group: Ti Data: Application of extreme learning machine for series compensated transmission line protection – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Malathi%2C+V%2E%22">Malathi, V.</searchLink><relatesTo>1</relatesTo><i> vmeee@autmdu.ac.in</i><br /><searchLink fieldCode="AR" term="%22Marimuthu%2C+N%2ES%2E%22">Marimuthu, N.S.</searchLink><relatesTo>2</relatesTo><i> sbeee@tce.edu</i><br /><searchLink fieldCode="AR" term="%22Baskar%2C+S%2E%22">Baskar, S.</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Ramar%2C+K%2E%22">Ramar, K.</searchLink><relatesTo>4</relatesTo><i> ramar@mmu.edu.my</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Engineering+Applications+of+Artificial+Intelligence%22">Engineering Applications of Artificial Intelligence</searchLink>. Aug2011, Vol. 24 Issue 5, p880-887. 8p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+lines+testing%22">Electric lines testing</searchLink><br /><searchLink fieldCode="DE" term="%22Wavelets+%28Mathematics%29%22">Wavelets (Mathematics)</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+fault+location%22">Electric fault location</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+currents%22">Electric currents</searchLink><br /><searchLink fieldCode="DE" term="%22Capacitors%22">Capacitors</searchLink><br /><searchLink fieldCode="DE" term="%22Robust+control%22">Robust control</searchLink><br /><searchLink fieldCode="DE" term="%22Feasibility+studies%22">Feasibility studies</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Abstract: This paper proposes a new approach based on combined Wavelet Transform-Extreme Learning Machine (WT-ELM) technique for fault section identification (whether the fault is before or after the series capacitor as observed from the relay point), classification and location in a series compensated transmission line. This method uses the samples of fault currents for half cycle duration from the inception of fault. The features of fault currents are extracted by first level decomposition of the current samples using discrete wavelet transform (DWT) and the extracted features are applied as inputs to ELMs for fault section identification, classification and location. The feasibility of the proposed method has been tested on a 400kV, 300km series compensated transmission line for all the ten types of faults using MATLAB simulink. On testing 28,800 fault cases with varying fault resistance, fault inception angle, fault distance, load angle, percentage compensation level and source impedance, the performance of the proposed method has been found to be quite promising. The results also indicate that the proposed method is robust to wide variation in system and operating conditions. [Copyright &y& Elsevier] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Engineering Applications of Artificial Intelligence is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.engappai.2011.03.003 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 8 StartPage: 880 Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Electric lines testing Type: general – SubjectFull: Wavelets (Mathematics) Type: general – SubjectFull: Electric fault location Type: general – SubjectFull: Electric currents Type: general – SubjectFull: Capacitors Type: general – SubjectFull: Robust control Type: general – SubjectFull: Feasibility studies Type: general Titles: – TitleFull: Application of extreme learning machine for series compensated transmission line protection Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Malathi, V. – PersonEntity: Name: NameFull: Marimuthu, N.S. – PersonEntity: Name: NameFull: Baskar, S. – PersonEntity: Name: NameFull: Ramar, K. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2011 Type: published Y: 2011 Identifiers: – Type: issn-print Value: 09521976 Numbering: – Type: volume Value: 24 – Type: issue Value: 5 Titles: – TitleFull: Engineering Applications of Artificial Intelligence Type: main |
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