Fundamentals of the C-DEEPSO Algorithm and its Application to the Reactive Power Optimization of Wind Farms
Fundamentals of the C-DEEPSO Algorithm and its Application to the Reactive Power Optimization of Wind Farms
dc.contributor.author | Marcelino,CG | en |
dc.contributor.author | Almeida,PEM | en |
dc.contributor.author | Wanner,EF | en |
dc.contributor.author | Leonel Magalhães Carvalho | en |
dc.contributor.author | Vladimiro Miranda | en |
dc.date.accessioned | 2018-01-14T16:17:36Z | |
dc.date.available | 2018-01-14T16:17:36Z | |
dc.date.issued | 2016 | en |
dc.description.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. | en |
dc.identifier.uri | http://repositorio.inesctec.pt/handle/123456789/6064 | |
dc.identifier.uri | http://dx.doi.org/10.1109/CEC.2016.7743973 | en |
dc.language | eng | en |
dc.relation | 4971 | en |
dc.relation | 208 | en |
dc.rights | info:eu-repo/semantics/openAccess | en |
dc.title | Fundamentals of the C-DEEPSO Algorithm and its Application to the Reactive Power Optimization of Wind Farms | en |
dc.type | conferenceObject | en |
dc.type | Publication | en |
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