Fundamentals of the C-DEEPSO Algorithm and its Application to the Reactive Power Optimization of Wind Farms

• Type: Article in International Conference Proceedings Book
• Date: 2016

Authors

Marcelino, CG (Author) Other; Almeida, PEM (Author) Other; Wanner, EF (Author) Other; Leonel Carvalho (Author) Other; Vladimiro Miranda (Author) FEUP

Conference proceedings International

Title: 2016 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC)

Pages: 1547-1554

IEEE Congress on Evolutionary Computation (CEC) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI)

Vancouver, CANADA, JUL 24-29, 2016

Indexing

ISI Web of Knowledge - 4 Citations

Other information

Authenticus ID: P-00M-7K6

DOI: 10.1109/CEC.2016.7743973

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 8