Intelligent fault analysis of transmission line using phasor measurement unit incorporating auto-reclosure protection scheme
Abstract The power system stability and reliability are stimulated by the faults on the transmission line. Many researchers have explored the performance of the transmission system under various kinds of faults. Specifically, the arrival of expeditious and effective data acquisition systems with hig...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2021-04-01
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| Series: | SN Applied Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s42452-021-04510-x |
| _version_ | 1818441535254953984 |
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| author | Kunjabihari Swain Murthy Cherukuri |
| author_facet | Kunjabihari Swain Murthy Cherukuri |
| author_sort | Kunjabihari Swain |
| collection | DOAJ |
| description | Abstract The power system stability and reliability are stimulated by the faults on the transmission line. Many researchers have explored the performance of the transmission system under various kinds of faults. Specifically, the arrival of expeditious and effective data acquisition systems with high rate of sampling has set down the foundation for successful real-time monitoring. Using the LabVIEW and the data acquisition system’s of National Instruments (NI), virtual systems have been developed for obtaining optimal paradigmatic data with appropriate characterization and quality transmission. The primary objective of the work is to perceive and comprehend the transmission line faults with the aid of synchronized phasor measurements obtained from the phasor measurement unit (PMU) as well as protecting the system using auto-reclosing signal. The developed algorithms include phaselet coefficients for perception as well as comprehension. In order to increase the accuracy, particle swarm optimized extreme learning machine technique has also been used for comprehension. A protection scheme is employed using auto-reclosing to minimize the power loss and quick reconnection the power line in case of temporary fault. Developed algorithms have been validated on a practical laboratory transmission line using NI PMU. As the LabVIEW platform has been used for simulations, it is composed of visual displays such that the system operator can efficiently perform the planning and control decisions. |
| first_indexed | 2024-12-14T18:29:48Z |
| format | Article |
| id | doaj.art-035077b7f44e49dc9a36edc731c16fe8 |
| institution | Directory Open Access Journal |
| issn | 2523-3963 2523-3971 |
| language | English |
| last_indexed | 2024-12-14T18:29:48Z |
| publishDate | 2021-04-01 |
| publisher | Springer |
| record_format | Article |
| series | SN Applied Sciences |
| spelling | doaj.art-035077b7f44e49dc9a36edc731c16fe82022-12-21T22:51:49ZengSpringerSN Applied Sciences2523-39632523-39712021-04-013511510.1007/s42452-021-04510-xIntelligent fault analysis of transmission line using phasor measurement unit incorporating auto-reclosure protection schemeKunjabihari Swain0Murthy Cherukuri1Electrical and Electronics Engineering, National Institute of Science and TechnologyElectrical and Electronics Engineering, National Institute of Science and TechnologyAbstract The power system stability and reliability are stimulated by the faults on the transmission line. Many researchers have explored the performance of the transmission system under various kinds of faults. Specifically, the arrival of expeditious and effective data acquisition systems with high rate of sampling has set down the foundation for successful real-time monitoring. Using the LabVIEW and the data acquisition system’s of National Instruments (NI), virtual systems have been developed for obtaining optimal paradigmatic data with appropriate characterization and quality transmission. The primary objective of the work is to perceive and comprehend the transmission line faults with the aid of synchronized phasor measurements obtained from the phasor measurement unit (PMU) as well as protecting the system using auto-reclosing signal. The developed algorithms include phaselet coefficients for perception as well as comprehension. In order to increase the accuracy, particle swarm optimized extreme learning machine technique has also been used for comprehension. A protection scheme is employed using auto-reclosing to minimize the power loss and quick reconnection the power line in case of temporary fault. Developed algorithms have been validated on a practical laboratory transmission line using NI PMU. As the LabVIEW platform has been used for simulations, it is composed of visual displays such that the system operator can efficiently perform the planning and control decisions.https://doi.org/10.1007/s42452-021-04510-xPhasor measurement unitPower system protectionFault perceptionFault comprehensionPhaseletExtreme learning machine |
| spellingShingle | Kunjabihari Swain Murthy Cherukuri Intelligent fault analysis of transmission line using phasor measurement unit incorporating auto-reclosure protection scheme SN Applied Sciences Phasor measurement unit Power system protection Fault perception Fault comprehension Phaselet Extreme learning machine |
| title | Intelligent fault analysis of transmission line using phasor measurement unit incorporating auto-reclosure protection scheme |
| title_full | Intelligent fault analysis of transmission line using phasor measurement unit incorporating auto-reclosure protection scheme |
| title_fullStr | Intelligent fault analysis of transmission line using phasor measurement unit incorporating auto-reclosure protection scheme |
| title_full_unstemmed | Intelligent fault analysis of transmission line using phasor measurement unit incorporating auto-reclosure protection scheme |
| title_short | Intelligent fault analysis of transmission line using phasor measurement unit incorporating auto-reclosure protection scheme |
| title_sort | intelligent fault analysis of transmission line using phasor measurement unit incorporating auto reclosure protection scheme |
| topic | Phasor measurement unit Power system protection Fault perception Fault comprehension Phaselet Extreme learning machine |
| url | https://doi.org/10.1007/s42452-021-04510-x |
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