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 publication

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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