Autoreclosure in extra high voltage lines using taguchi's method and optimized neural networks
This paper presents a method to discriminate the temporary faults from the permanent ones in an extra high voltage (EHV) transmission line so that improper reclosing of the line onto a fault is avoided. The fault identification prior to reclosing is based on optimized artificial neural network assoc...
| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2008
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| Subjects: | |
| Online Access: | http://scholars.utp.edu.my/id/eprint/275/ http://scholars.utp.edu.my/id/eprint/275/1/paper.pdf |
| _version_ | 1848658948151836672 |
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| author | K.S.R, Rao F. D., Zahlay |
| author_facet | K.S.R, Rao F. D., Zahlay |
| author_sort | K.S.R, Rao |
| building | UTP Institutional Repository |
| collection | Online Access |
| description | This paper presents a method to discriminate the temporary faults from the permanent ones in an extra high voltage (EHV) transmission line so that improper reclosing of the line onto a fault is avoided. The fault identification prior to reclosing is based on optimized artificial neural network associated with standard Error Back-Propagation, Levenberg Marquardt Algorithm and Resilient Back-Propagation training algorithms together with Taguchi's Method. The algorithms are developed using MATLAB™ software. A range of faults are simulated on EHV modeled transmission line using SimPowerSytems™, and the spectra of the fault data are analyzed using fast Fourier transform which facilitates extraction of distinct features of each type of fault. For both training and testing purposes, the neural network is fed with the normalized energies of the DC component, the fundamental and the first four harmonics of the faulted voltages. The developed algorithm is effectively trained, verified and validated with a set of training, dedicated testing and validation data respectively. © 2008 IEEE.
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| first_indexed | 2025-11-13T07:22:38Z |
| format | Conference or Workshop Item |
| id | oai:scholars.utp.edu.my:275 |
| institution | Universiti Teknologi Petronas |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-13T07:22:38Z |
| publishDate | 2008 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:scholars.utp.edu.my:2752017-01-19T08:26:32Z http://scholars.utp.edu.my/id/eprint/275/ Autoreclosure in extra high voltage lines using taguchi's method and optimized neural networks K.S.R, Rao F. D., Zahlay TK Electrical engineering. Electronics Nuclear engineering This paper presents a method to discriminate the temporary faults from the permanent ones in an extra high voltage (EHV) transmission line so that improper reclosing of the line onto a fault is avoided. The fault identification prior to reclosing is based on optimized artificial neural network associated with standard Error Back-Propagation, Levenberg Marquardt Algorithm and Resilient Back-Propagation training algorithms together with Taguchi's Method. The algorithms are developed using MATLAB™ software. A range of faults are simulated on EHV modeled transmission line using SimPowerSytems™, and the spectra of the fault data are analyzed using fast Fourier transform which facilitates extraction of distinct features of each type of fault. For both training and testing purposes, the neural network is fed with the normalized energies of the DC component, the fundamental and the first four harmonics of the faulted voltages. The developed algorithm is effectively trained, verified and validated with a set of training, dedicated testing and validation data respectively. © 2008 IEEE. 2008 Conference or Workshop Item NonPeerReviewed application/pdf en http://scholars.utp.edu.my/id/eprint/275/1/paper.pdf K.S.R, Rao and F. D., Zahlay (2008) Autoreclosure in extra high voltage lines using taguchi's method and optimized neural networks. In: 2008 IEEE Electrical Power and Energy Conference - Energy Innovation, 6 October 2008 through 7 October 2008, Vancouver, BC. http://www.scopus.com/inward/record.url?eid=2-s2.0-63049089484&partnerID=40&md5=afc755f7916d29adf47db5dbf7ed6912 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering K.S.R, Rao F. D., Zahlay Autoreclosure in extra high voltage lines using taguchi's method and optimized neural networks |
| title | Autoreclosure in extra high voltage lines using taguchi's method and optimized neural networks
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| title_full | Autoreclosure in extra high voltage lines using taguchi's method and optimized neural networks
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| title_fullStr | Autoreclosure in extra high voltage lines using taguchi's method and optimized neural networks
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| title_full_unstemmed | Autoreclosure in extra high voltage lines using taguchi's method and optimized neural networks
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| title_short | Autoreclosure in extra high voltage lines using taguchi's method and optimized neural networks
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| title_sort | autoreclosure in extra high voltage lines using taguchi's method and optimized neural networks |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://scholars.utp.edu.my/id/eprint/275/ http://scholars.utp.edu.my/id/eprint/275/ http://scholars.utp.edu.my/id/eprint/275/1/paper.pdf |