Optimizing the Generator Start-up Sequence After a Power System Blackout

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  author =       "Cong Shen and Paul Kaufmann and Martin Braun",
  title =        "Optimizing the Generator Start-up Sequence After a
                 Power System Blackout",
  booktitle =    "2014 IEEE PES General Meeting | Conference
  year =         "2014",
  keywords =     "genetic algorithms, genetic programming, network
                 Restoration, Generator Start-Up Sequence, VIKOR
                 Algorithm, Analytic Hierarchy Process (AHP),
                 Performance Index",
  ISSN =         "1932-5517",
  DOI =          "doi:10.1109/PESGM.2014.6938799",
  size =         "5 page",
  abstract =     "The restoration process of a power system after a
                 blackout consists of three phases, namely starting up
                 the generators, re-energizing the network, and picking
                 the loads. The generator start-up sequence is pivotal
                 for the total restoration time and the following
                 restoration steps. In this paper, a novel algorithm for
                 optimizing a generator start-up sequence is proposed.
                 Based on the VIKOR method, which is a multi-objective
                 approach, the proposed algorithm is not only able to
                 improve the start-up time and reliability of a
                 generator start-up sequence, but can also handle
                 auxiliary optimization criteria with priorities that
                 change during the start-up. Such criteria are, for
                 instance, the importance of the power increasing rate,
                 reliability and surrounding topologies of generators.
                 The efficiency of the proposed method is evaluated in
                 two tests. In the first test exhaustive search is used
                 to compute optimal solutions. In the second and larger
                 test NSGA-II is used to approximate the Pareto
                 frontier. For both tests the proposed algorithm
                 approximates the reference results while being
                 computationally very efficient. This makes it suitable
                 for online decision making where a new startup sequence
                 has to be computed in a few seconds or minutes.",
  notes =        "Also known as \cite{6938799}",

Genetic Programming entries for Cong Shen Paul Kaufmann Martin Braun