Title :
Artificial Neural Networks in Power System Restoration
Author :
Bretas, Arturo S. ; Phadke, Arun G.
Author_Institution :
University of Sao Paulo, Sao Paulo, Brazil; Virginia Polytechnic Institute and State University, Blacksburg, Virginia
Abstract :
Power system restoration (PSR) has been a subject of study for many years. In recent years many techniques were proposed to solve the limitations of the predetermined restoration guidelines and procedures used by a majority of system operators to restore a system following the occurrence of a wide area disturbance. This paper discusses limitations encountered in some currently used PSR techniques and a proposed improvement based on artificial neural networks (ANN). The proposed scheme is tested on a 162 bus transmission system and compared with a breadth-search restoration scheme. The results indicate that the use of ANN in power system restoration is a feasible option that should be considered for real-time applications.
Keywords :
Artificial neural networks; Electrodes; Grounding; Impedance; Load modeling; Power system modeling; Power system relaying; Power system restoration; Protection; Protective relaying; Artificial neural networks; artificial intelligence; power system restoration; wide-area disturbances;
Journal_Title :
Power Engineering Review, IEEE
DOI :
10.1109/MPER.2002.4311755