F. S. Avelar, P. C. Fritzen, M. A. A. Furucho, R. C. Betini




A computational model for self-recovery of electricity distribution network was developed to simulate it, emulated by the IEEE 123 nodes model. The electrical system considered has automatic switches capable of identifying a momentary fault in the line and finding the best reconfiguration for its reclosing. An artificial neural network (ANN), backpropagation, was used to classify the type of failure and determine the best reconfiguration of the distribution network. Initially, five power failure scenarios were simulated in certain different parts of the power grid, and power flow analysis via OpenDSS was performed. Following, the most suitable switching was observed within the shortest time interval to restore the power supply. In this way it is possible to identify the faulted segment in order to isolate it, leaving the smallest number of consumers in the shortest possible time without power supply. With the results of the simulations, tests and analyzes were performed to verify their robustness and speed, in the expectation that the model developed, be faster than an experienced Operator of a Distribution Center.

Published in: Renewable Energy & Power Quality Journal (RE&PQJ, Nº. 16)
Pages: 237-241 Date of Publication: 2018/04/20
ISSN: 2172-038X Date of Current Version:2018/03/23
REF: 272-18 Issue Date: April 2018
DOI:10.24084/repqj16.272 Publisher: EA4EPQ

Authors and affiliations

F. S. Avelar, P. C. Fritzen, M. A. A. Furucho, R. C. Betini
Department of Electrical Engineering. Federal University of Technology - Paraná. Campus Curitiba (Brazil)

Key words

Distribution Networks, Optimization, Self-recovery of networks, Smart Grid.


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