A Smart Online Over-Voltage Monitoring and Identification System

• Main Authors: Wang, Jing (Author), Yang, Qing (Contributor), Sima, Wenxia (Author), Yuan, Tao (Author), Zahn, Markus (Contributor)
• Other Authors: Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor), Massachusetts Institute of Technology. High Voltage Research Laboratory (Contributor), Massachusetts Institute of Technology. Laboratory for Electromagnetic and Electronic Systems (Contributor), Massachusetts Institute of Technology. Research Laboratory of Electronics (Contributor)
• Format: Article
• Language: English
• Published: Molecular Diversity Preservation International, 2011-10-04T18:46:42Z.
• Subjects: Article
• Online Access: Get fulltext

 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.