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

doi:10.1109/CEC.2016.7743973

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

Centro Federal de Educação Tecnológica de Minas Gerais
INESC TEC- Institute for Systems and Computer Engineering, Technology and Science

Resultado de pesquisa › revisão de pares

12 Citações (Scopus)

Resumo

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.

Idioma original	Inglês
Título da publicação do anfitrião	2016 IEEE Congress on Evolutionary Computation, CEC 2016
Editora	Institute of Electrical and Electronics Engineers Inc.
Páginas	1547-1554
Número de páginas	8
ISBN (eletrónico)	9781509006229
DOIs	https://doi.org/10.1109/CEC.2016.7743973
Estado da publicação	Publicadas - 14 nov. 2016
Publicado externamente	Sim
Evento	2016 IEEE Congress on Evolutionary Computation, CEC 2016 - Vancouver Duração: 24 jul. 2016 → 29 jul. 2016

Série de publicação

Nome	2016 IEEE Congress on Evolutionary Computation, CEC 2016

Conferência

Conferência	2016 IEEE Congress on Evolutionary Computation, CEC 2016
País/Território	Canada
Cidade	Vancouver
Período	24/07/16 → 29/07/16

Nota bibliográfica

Publisher Copyright:
© 2016 IEEE.

ODS da ONU

Este resultado contribui para o(s) seguinte(s) Objetivo(s) de Desenvolvimento Sustentável

ODS 7 Energia acessível e limpa

Acesso ao documento

10.1109/CEC.2016.7743973

Outros arquivos e links

Link to publication in Scopus

Citar isto

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. Em 2016 IEEE Congress on Evolutionary Computation, CEC 2016 (pp. 1547-1554). Artigo 7743973 (2016 IEEE Congress on Evolutionary Computation, CEC 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC.2016.7743973

Marcelino, Carolina G. ; Almeida, Paulo E.M. ; Wanner, Elizabeth F. et al. / Fundamentals of the C-DEEPSO algorithm and its application to the reactive power optimization of wind farms. 2016 IEEE Congress on Evolutionary Computation, CEC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 1547-1554 (2016 IEEE Congress on Evolutionary Computation, CEC 2016).

@inproceedings{b942618a6d2a4eeaab6b05fbdd6530d3,

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

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.",

author = "Marcelino, \{Carolina G.\} and Almeida, \{Paulo E.M.\} and Wanner, \{Elizabeth F.\} and Carvalho, \{Leonel M.\} and Vladimiro Miranda",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE Congress on Evolutionary Computation, CEC 2016 ; Conference date: 24-07-2016 Through 29-07-2016",

year = "2016",

month = nov,

day = "14",

doi = "10.1109/CEC.2016.7743973",

language = "English",

series = "2016 IEEE Congress on Evolutionary Computation, CEC 2016",

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}

Marcelino, CG, Almeida, PEM, Wanner, EF, Carvalho, LM & Miranda, V 2016, Fundamentals of the C-DEEPSO algorithm and its application to the reactive power optimization of wind farms. em 2016 IEEE Congress on Evolutionary Computation, CEC 2016., 7743973, 2016 IEEE Congress on Evolutionary Computation, CEC 2016, Institute of Electrical and Electronics Engineers Inc., pp. 1547-1554, 2016 IEEE Congress on Evolutionary Computation, CEC 2016, Vancouver, Canada, 24/07/16. https://doi.org/10.1109/CEC.2016.7743973

Fundamentals of the C-DEEPSO algorithm and its application to the reactive power optimization of wind farms. / Marcelino, Carolina G.; Almeida, Paulo E.M.; Wanner, Elizabeth F. et al.
2016 IEEE Congress on Evolutionary Computation, CEC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 1547-1554 7743973 (2016 IEEE Congress on Evolutionary Computation, CEC 2016).

Resultado de pesquisa › revisão de pares

TY - GEN

T1 - Fundamentals of the C-DEEPSO algorithm and its application to the reactive power optimization of wind farms

AU - Marcelino, Carolina G.

AU - Almeida, Paulo E.M.

AU - Wanner, Elizabeth F.

AU - Carvalho, Leonel M.

AU - Miranda, Vladimiro

PY - 2016/11/14

Y1 - 2016/11/14

N2 - 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.

AB - 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.

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BT - 2016 IEEE Congress on Evolutionary Computation, CEC 2016

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T2 - 2016 IEEE Congress on Evolutionary Computation, CEC 2016

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ER -

Marcelino CG, Almeida PEM, Wanner EF, Carvalho LM, Miranda V. Fundamentals of the C-DEEPSO algorithm and its application to the reactive power optimization of wind farms. Em 2016 IEEE Congress on Evolutionary Computation, CEC 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 1547-1554. 7743973. (2016 IEEE Congress on Evolutionary Computation, CEC 2016). doi: 10.1109/CEC.2016.7743973

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

Resumo

Série de publicação

Conferência

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ODS da ONU

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