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Title [sv]
Vågparksoptimering genom spridningsteori och samverkande styrning för multipla kluster
Title [en]
Multiple cluster scattering theory and collaborative control for wave power optimization
Abstract [en]
Ocean waves provide a promising source of renewable energy, but no large-scale wave power technology has yet demonstrated high efficiency and competitive costs. Main challenges for achieving this are that the optimization algorithms of wave power farms require fast hydrodynamics models, but all fast hydrodynamics models for large farms are too approximate; that control methods can greatly enhance the performance, but in general require wave forecasting which is difficult to obtain in reality; and that the cost functions used in the optimization should take into account not only the power output which is the standard today, but instead optimize upon multiple objectives such as lifetime costs, electricity quality, and constraints on the devices.The proposed project addresses these problem with new approaches to modelling and optimizing large-scale wave power systems. A multiple cluster scattering theory will be developed and validated based on the world-leading hydrodynamics model developed by the applicant. A collaborative machine learning control method will enhance the performance of the farm, based on promising preliminary work. The model will be combined with state-of-the-art optimization algorithms and economical models with unique input in the economical cost function. The resulting accurate and computationally fast model will be used to find efficient and competitive configurations of large-scale conversion of ocean waves to electricity.
Publications (1 of 1) Show all publications
Shahroozi, Z., Göteman, M. & Engström, J. (2023). A Neural Network Approach To Minimize Line Forces In The Survivability Of The Point-Absorber Wave Energy Converters. In: Proceedings of ASME 2023 42nd International Conference on Ocean, Offshore & Arctic Engineering (OMAE2023): . Paper presented at International Conference on Ocean, Offshore & Arctic Engineering (OMAE), 11-16 June, 2023, Melbourne, Australia. ASME Press, 8, Article ID OMAE2023-102422.
Open this publication in new window or tab >>A Neural Network Approach To Minimize Line Forces In The Survivability Of The Point-Absorber Wave Energy Converters
2023 (English)In: Proceedings of ASME 2023 42nd International Conference on Ocean, Offshore & Arctic Engineering (OMAE2023), ASME Press, 2023, Vol. 8, article id OMAE2023-102422Conference paper, Published paper (Refereed)
Abstract [en]

One strategy for the survivability of wave energy converters(WECs) is to minimize the extreme forces on the structure by adjusting the system damping. In this paper, a neural network model is developed to predict the peak line force for a 1:30 scaled point-absorber WEC with a linear friction-damping power take-off (PTO). The algorithm trains over the wave tank experimental data and enables an update of the system damping based on the system state (i.e. position, velocity, and acceleration) and information on the incoming waves for the extreme sea states. The results show that the deep neural network (DNN) developed here is relatively fast and able to predict the peak line forces with a correlation of 88% when compared to the true (experimental)data. Then, the optimum damping for survivability purposes is found by minimizing the peak line force. It is shown that the optimum damping varies depending on the system state in each zero up-crossing episode.

Place, publisher, year, edition, pages
ASME Press, 2023
National Category
Control Engineering Marine Engineering Ocean and River Engineering
Identifiers
urn:nbn:se:uu:diva-506611 (URN)10.1115/OMAE2023-102422 (DOI)001216330300065 ()978-0-7918-8690-8 (ISBN)
Conference
International Conference on Ocean, Offshore & Arctic Engineering (OMAE), 11-16 June, 2023, Melbourne, Australia
Funder
Swedish Energy Agency, 47264-1Swedish Research Council, 2020-03634StandUpÅForsk (Ångpanneföreningen's Foundation for Research and Development)
Available from: 2023-06-28 Created: 2023-06-28 Last updated: 2024-06-12Bibliographically approved
Principal InvestigatorGöteman, Malin
Coordinating organisation
Uppsala University
Funder
Period
2021-01-01 - 2024-12-31
National Category
Energy SystemsMarine Engineering
Identifiers
DiVA, id: project:6513Project, id: 2020-03634_VR

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