Title page for ETD etd-12052001-101959


Type of Document Dissertation
Author Bretas, Arturo Suman
Author's Email Address abretas@vt.edu
URN etd-12052001-101959
Title Robust Electric Power Infrastructures. Response and Recovery during Catastrophic Failures.
Degree PhD
Department Electrical and Computer Engineering
Advisory Committee
Advisor Name Title
Phadke, Arun G. Committee Chair
De La Ree Lopez, Jaime Committee Member
Kohler, Werner E. Committee Member
Liu, Yilu Committee Member
VanLandingham, Hugh F. Committee Member
Keywords
  • Artificial Neural Networks
  • Power System Restoration
Date of Defense 2001-12-04
Availability unrestricted
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.

The PSR technique implemented is composed by several pairs of ANN, each one assigned to an individual island of the system. The restoration of the system is done in parallel in each island. After each island is restored the tie lines are closed. The results encountered shows that ANN based schemes can be used in PSR helping the operators restore the system under the stressful conditions following a blackout.

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