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A Smart Online Over-Voltage Monitoring and Identification System
State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing, China
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; in revised form: 6 April 2011 / Accepted: 15 April 2011 / Published: 18 April 2011
(This article belongs to the Special Issue Future Grid
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.
Keywords: smart grid; over-voltage; identification; S-transform; fuzzy expert system
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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.
Wang J, Yang Q, Sima W, Yuan T, Zahn M. A Smart Online Over-Voltage Monitoring and Identification System. Energies. 2011; 4(4):599-615.
Wang, Jing; Yang, Qing; Sima, Wenxia; Yuan, Tao; Zahn, Markus. 2011. "A Smart Online Over-Voltage Monitoring and Identification System." Energies 4, no. 4: 599-615.