Fundamentals of the C-DEEPSO algorithm and its application to the reactive power optimization of wind farms

Carolina G. Marcelino, Paulo E.M. Almeida, Elizabeth F. Wanner, Leonel M. Carvalho, Vladimiro Miranda

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, a novel hybrid single-objective metaheuristic, the so called C-DEEPSO (Canonical Differential Evolutionary Particle Swarm Optimization), is proposed and tested. C-DEEPSO can be viewed as an evolutionary algorithm with recombination rules borrowed from PSO, or a swarm optimization method with selection and self-adaptiveness properties proper from DE. A case study on the problem of optimal control for reactive sources in energy production by Wind Power Plants (WPP), solved by means of Optimal Power Flow (OPF-like), is used to test the new hybrid algorithm and to evaluate its performance. C-DEEPSO is compared to the baseline algorithm, DEEPSO, and to a reference algorithm, Mean-Variance Mapping Optimization (MVMO). The experiments indicate that the proposed algorithm is efficient and competitive, capable to tackle this large-scale problem. The results also show that the new approach exhibits better results, when compared to MVMO.

Original languageEnglish
Title of host publication2016 IEEE Congress on Evolutionary Computation (CEC)
PublisherIEEE
Pages1547-1554
Number of pages8
ISBN (Electronic)978-1-5090-0622-9
DOIs
Publication statusPublished - 14 Nov 2016
Event2016 IEEE Congress on Evolutionary Computation - Vancouver, Canada
Duration: 24 Jul 201629 Jul 2016

Congress

Congress2016 IEEE Congress on Evolutionary Computation
Abbreviated titleCEC 2016
CountryCanada
CityVancouver
Period24/07/1629/07/16

Bibliographical note

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  • Research Output

    • 3 Conference contribution

    A quadratic approximation-based local search operator for handling two equality constraints in continuous optimization problems

    Fonseca, C. H. & Wanner, E. F., 14 Nov 2016, 2016 IEEE Congress on Evolutionary Computation (CEC). IEEE, p. 4911-4917 7 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  • Multiobjective approach to the vehicle routing problem with demand responsive transport

    Mendes, R. S., Miranda, D. S., Wanner, E. F., Sarubbi, J. F. M. & Martins, F. V. C., 14 Nov 2016, 2016 IEEE Congress on Evolutionary Computation (CEC). IEEE, p. 3761-3768 8 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  • Portfolio selection for open-pit mining assets acquisition

    Ferreira, L. S., Wanner, E. F., Lisboa, A. C. & Vieira, D. A. G., 14 Nov 2016, 2016 IEEE Congress on Evolutionary Computation (CEC). IEEE, p. 1525-1532 8 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  • Cite this

    Marcelino, C. G., Almeida, P. E. M., Wanner, E. F., Carvalho, L. M., & Miranda, V. (2016). Fundamentals of the C-DEEPSO algorithm and its application to the reactive power optimization of wind farms. In 2016 IEEE Congress on Evolutionary Computation (CEC) (pp. 1547-1554). IEEE. https://doi.org/10.1109/CEC.2016.7743973