06-09-2012, 02:58 PM
Robust Electric Power Infrastructures. Response and Recovery during Catastrophic Failure
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ABSTRACT
This dissertation is a systematic study of artificial neural networks (ANN) applications in
power system restoration (PSR). PSR is based on available generation and load to be restored
analysis. A literature review showed that the conventional PSR methods, i.e. the pre-established
guidelines, the expert systems method, the mathematical programming method and the petri-net
method have limitations such as the necessary time to obtain the PSR plan. ANN may help to
solve this problem presenting a reliable PSR plan in a smaller time.
Based on actual and past experiences, a PSR engine based on ANN was proposed and
developed. Data from the Iowa 162 bus power system was used in the implementation of the
technique. Reactive and real power balance, fault location, phase angles across breakers and
intentional islanding were taken into account in the implementation of the technique. Constraints
in PSR as thermal limits of transmission lines (TL), stability issues, number of TL used in the
restoration plan and lockout breakers were used to create feasible PSR plans. To compare the
time necessary to achieve the PSR plan with another technique a PSR method based on a
breadth-search algorithm was implemented. This algorithm was also used to create training and
validation patterns for the ANN used in the scheme. An algorithm to determine the switching
sequence of the breakers was also implemented. In order to determine the switching sequence of
the breakers the algorithm takes into account the most priority loads and the final system
configuration generated by the ANN.