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

College of Engineering and Physical Sciences

Research output: Chapter in Book/Published conference output › Conference 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 language	English
Title of host publication	2016 IEEE Congress on Evolutionary Computation (CEC)
Publisher	IEEE
Pages	1547-1554
Number of pages	8
ISBN (Electronic)	978-1-5090-0622-9
DOIs	https://doi.org/10.1109/CEC.2016.7743973
Publication status	Published - 14 Nov 2016
Event	2016 IEEE Congress on Evolutionary Computation - Vancouver, Canada Duration: 24 Jul 2016 → 29 Jul 2016

Congress

Congress	2016 IEEE Congress on Evolutionary Computation
Abbreviated title	CEC 2016
Country/Territory	Canada
City	Vancouver
Period	24/07/16 → 29/07/16

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10.1109/CEC.2016.7743973

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@inproceedings{f0d7e057938e44ff8bc0fb4dd90d4bc3,

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 = "-; 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",

pages = "1547--1554",

booktitle = "2016 IEEE Congress on Evolutionary Computation (CEC)",

publisher = "IEEE",

address = "United States",

}

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. in 2016 IEEE Congress on Evolutionary Computation (CEC). IEEE, pp. 1547-1554, 2016 IEEE Congress on Evolutionary Computation, Vancouver, Canada, 24/07/16. https://doi.org/10.1109/CEC.2016.7743973

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

N1 - -

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.

UR - http://ieeexplore.ieee.org/document/7743973/

UR - http://www.scopus.com/inward/record.url?scp=85008244094&partnerID=8YFLogxK

U2 - 10.1109/CEC.2016.7743973

DO - 10.1109/CEC.2016.7743973

M3 - Conference publication

AN - SCOPUS:85008244094

SP - 1547

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BT - 2016 IEEE Congress on Evolutionary Computation (CEC)

PB - IEEE

T2 - 2016 IEEE Congress on Evolutionary Computation

Y2 - 24 July 2016 through 29 July 2016

ER -

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

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