A Hybrid Multi-objective Evolutionary Approach for Power Grid Topology Design

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 12 - Chapter in an edited book (Author)

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Detail(s)

Original languageEnglish
Title of host publicationEvolutionary Algorithms, Swarm Dynamics and Complex Networks
EditorsIvan Zelinka, Guanrong Chen
PublisherSpringer-Verlag Berlin Heidelberg
Chapter13
Pages265-284
ISBN (electronic)9783662556634
ISBN (print)9783662556610
Publication statusPublished - 2018

Publication series

NameEmergence, Complexity and Computation
Volume26
ISSN (Print)2194-7287

Abstract

Power grid is one of the critical infrastructures in human society. It is highly complex in both structure and dynamics. In order to study its performance, different models, such as Kuramoto oscillator network model, power flow model, cascading load model and so on, have been suggested. In this chapter, it is to demonstrate how an evolutionary algorithm can be applied to effectively solve the topological design problem in power grid based on the Kuramoto oscillator network model. Recognizing that multiple criteria are commonly confronted in practice, a multiobjective evolutionary algorithm is developed. Two objectives, namely the network synchronizability and the cost, are considered in this work. In addition, since the design problem is complex and nonlinear, a dedicated local searching mechanism is embedded to enhance the searching capability of the algorithm. Finally, the effectiveness of the proposed algorithm is confirmed by extensive numerical simulations.

Citation Format(s)

A Hybrid Multi-objective Evolutionary Approach for Power Grid Topology Design. / Bi, Xiaowen; Tang, Wallace K.S.
Evolutionary Algorithms, Swarm Dynamics and Complex Networks. ed. / Ivan Zelinka; Guanrong Chen. Springer-Verlag Berlin Heidelberg, 2018. p. 265-284 (Emergence, Complexity and Computation; Vol. 26).

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 12 - Chapter in an edited book (Author)