A Smart Online Over-Voltage Monitoring and Identification System
This paper proposes a complete and effective smart over-voltage monitoring and identification system. In recent years, smart grids are of the greatest interest in power system research. One of the main features of smart grid is their self-healing, which can continuously carry out online self-eval...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Molecular Diversity Preservation International
2011
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| Online Access: | http://hdl.handle.net/1721.1/66178 https://orcid.org/0000-0003-2228-2347 |
| _version_ | 1826209198252228608 |
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| author | Wang, Jing Yang, Qing Sima, Wenxia Yuan, Tao Zahn, Markus |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Wang, Jing Yang, Qing Sima, Wenxia Yuan, Tao Zahn, Markus |
| author_sort | Wang, Jing |
| collection | MIT |
| description | This paper proposes a complete and effective smart over-voltage monitoring and
identification system. In recent years, smart grids are of the greatest interest in power system
research. One of the main features of smart grid is their self-healing, which can continuously
carry out online self-evaluation, discover existing faults, and correct them immediately. The
over-voltage smart monitoring-identification-suppression systems play a key role in the
construction of self-healing grids. In this paper, eight kinds of common over-voltage are
discussed and analyzed. The S-transform algorithm is used to extract features of
over-voltage. Aiming at the main features of each kind of over-voltage, six different
characteristic quantities are proposed. A well designed fuzzy expert system and a support
vector machine are employed as the classifiers to build a two-step identification model. The
accuracy of the identification system is verified by field records. Results show that this
system is feasible and promising for real applications. |
| first_indexed | 2024-09-23T14:18:45Z |
| format | Article |
| id | mit-1721.1/66178 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T14:18:45Z |
| publishDate | 2011 |
| publisher | Molecular Diversity Preservation International |
| record_format | dspace |
| spelling | mit-1721.1/661782022-09-29T08:36:43Z A Smart Online Over-Voltage Monitoring and Identification System Wang, Jing Yang, Qing Sima, Wenxia Yuan, Tao Zahn, Markus Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology. High Voltage Research Laboratory Massachusetts Institute of Technology. Laboratory for Electromagnetic and Electronic Systems Massachusetts Institute of Technology. Research Laboratory of Electronics Zahn, Markus Yang, Qing Zahn, Markus This paper proposes a complete and effective smart over-voltage monitoring and identification system. In recent years, smart grids are of the greatest interest in power system research. One of the main features of smart grid is their self-healing, which can continuously carry out online self-evaluation, discover existing faults, and correct them immediately. The over-voltage smart monitoring-identification-suppression systems play a key role in the construction of self-healing grids. In this paper, eight kinds of common over-voltage are discussed and analyzed. The S-transform algorithm is used to extract features of over-voltage. Aiming at the main features of each kind of over-voltage, six different characteristic quantities are proposed. A well designed fuzzy expert system and a support vector machine are employed as the classifiers to build a two-step identification model. The accuracy of the identification system is verified by field records. Results show that this system is feasible and promising for real applications. National Basic Research Program of China (973 Program) (2009CB724504) National 111 Project of China (B08036) 2011-10-04T18:46:42Z 2011-10-04T18:46:42Z 2011-04 2011-04 Article http://purl.org/eprint/type/JournalArticle 1996-1073 http://hdl.handle.net/1721.1/66178 Wang, Jing et al. “A Smart Online Over-Voltage Monitoring and Identification System.” Energies 4 (2011): 599-615. © 2011 by the authors https://orcid.org/0000-0003-2228-2347 en_US http://dx.doi.org/10.3390/en4040599 Energies Creative Commons Attribution 3.0 http://creativecommons.org/licenses/by/3.0/ application/pdf Molecular Diversity Preservation International MDPI |
| spellingShingle | Wang, Jing Yang, Qing Sima, Wenxia Yuan, Tao Zahn, Markus A Smart Online Over-Voltage Monitoring and Identification System |
| title | A Smart Online Over-Voltage Monitoring and Identification System |
| title_full | A Smart Online Over-Voltage Monitoring and Identification System |
| title_fullStr | A Smart Online Over-Voltage Monitoring and Identification System |
| title_full_unstemmed | A Smart Online Over-Voltage Monitoring and Identification System |
| title_short | A Smart Online Over-Voltage Monitoring and Identification System |
| title_sort | smart online over voltage monitoring and identification system |
| url | http://hdl.handle.net/1721.1/66178 https://orcid.org/0000-0003-2228-2347 |
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