DocumentCode :
2611718
Title :
Topology error identification for the NEPTUNE power system using an artificial neural network
Author :
Schneider, Kevin ; Liu, Chen-Ching
Author_Institution :
Dept. of Electr. Eng., Washington Univ., Seattle, WA, USA
fYear :
2004
fDate :
10-13 Oct. 2004
Firstpage :
60
Abstract :
The goal of the North Eastern Pacific Time-Series Undersea Networked Experiment (NEPTUNE) is to construct a cabled observatory on the floor of the Pacific Ocean, encompassing the Juan de Fuca Tectonic Plate. The power system associated with the proposed observatory is unlike conventional terrestrial power systems in many ways due to the unique operating conditions of cabled observatories. The unique operating conditions of the system require hardware and software applications that are not found in terrestrial power systems. This paper builds upon earlier work and describes a method for topology error identification in the NEPTUNE system that utilizes an artificial neural network (ANN) to determine single contingency topology errors.
Keywords :
DC transmission networks; error analysis; neural nets; observers; power engineering computing; power system state estimation; submarine cables; ANN; DC power system; Juan de Fuca Tectonic Plate; NEPTUNE; North Eastern Pacific Time-Series Undersea Networked Experiment; artificial neural network; cabled observatory; error identification; hardware application; software application; state estimation; underwater equipment; Artificial neural networks; Hardware; Network topology; Observatories; Oceans; Power system interconnection; Power system modeling; Power systems; Underwater cables; Voltage;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power Systems Conference and Exposition, 2004. IEEE PES
Print_ISBN :
0-7803-8718-X
Type :
conf
DOI :
10.1109/PSCE.2004.1397439
Filename :
1397439
Link To Document :
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