E3S Web Conf.
Volume 191, 20202020 The 3rd International Conference on Renewable Energy and Environment Engineering (REEE 2020)
|Number of page(s)||6|
|Section||Modern Electronic Technology and Application|
|Published online||24 September 2020|
Optimization of the electrical connection topology of a tidal farm network
1 Laboratoire Universitaire des Sciences Appliquées de Cherbourg (LUSAC), Normandie Univ, UNICAEN, 14000 Caen France.
2 Institut de Recherche en Énergie Électrique de Nantes Atlantique (IREENA), Université de Nantes, 44602 Saint Nazaire, France
* Corresponding author: email@example.com
This article presents an approach to optimize the electrical connection topology of tidal energy converters in a tidal farm. The methodology is based on a genetic algorithm (GA). The main purpose is to present a technique of coding to find the best electrical connection topology of the tidal farm network. The optimization model takes into account the energy loss in the submarine cables. The model gives as its output the optimal number of turbine clusters connected to each offshore substation, the number of turbines in each cluster, the cross-section of MV and HV cables, the connection design for each cluster of turbines as well as the number and the locations of the offshore substations. A particle swarm optimization algorithm (PSO) is used to confirm the results obtained with the GA. The optimization approach is applied to the Fromveur Strait (France).
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.