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Energies 2011, 4(4), 599-615; doi:10.3390/en4040599

A Smart Online Over-Voltage Monitoring and Identification System

1
State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing, China
2
Research Laboratory of Electronics, Laboratory for Electromagnetic and Electronic Systems, High Voltage Research Laboratory, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
*
Author to whom correspondence should be addressed.
Received: 24 February 2011 / Revised: 6 April 2011 / Accepted: 15 April 2011 / Published: 18 April 2011
(This article belongs to the Special Issue Future Grid)
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Abstract

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. View Full-Text
Keywords: smart grid; over-voltage; identification; S-transform; fuzzy expert system smart grid; over-voltage; identification; S-transform; fuzzy expert system
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Wang, J.; Yang, Q.; Sima, W.; Yuan, T.; Zahn, M. A Smart Online Over-Voltage Monitoring and Identification System. Energies 2011, 4, 599-615.

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