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Control of a point absorber wave energy converter in extreme wave conditions using a deep learning model in WEC-Sim
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Electrical Engineering, Electricity. (wave power group)ORCID iD: 0000-0002-1165-5569
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Electrical Engineering, Electricity. (Wave power group)ORCID iD: 0000-0001-9213-6447
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Electrical Engineering, Electricity. (Wave power group)ORCID iD: 0000-0002-2031-8134
2023 (English)In: OCEANS 2023 - LIMERICK, IEEE, 2023Conference paper, Published paper (Refereed)
Abstract [en]

The survivability of wave energy converters (WECs) is one of the challenges that have a direct influence on their cost. To protect the WEC from the impact of extreme waves, it is often to over-dimension the components or adopt survivability modes e.g. by submerging or lifting the WEC if it is applicable. Here, a control strategy for adjusting the system damping is developed based on deep neural networks (DNN) to minimize the line (mooring) force exerted on a 1:30 scaled WEC. This DNN model is then implemented in a control system of a numerical WEC-Sim model to find the optimal power take-off (PTO) damping for every zero up-crossing episode of surface elevation which minimizes the peak line force. The WEC-Sim model was calibrated based on a 1:30 scaled wave tank experiment that was designed to investigate the WEC response in extreme sea states with a 50-year return period. It is shown that this survival strategy reduces the peak forces when compared with the response of a system that has been set to a constant PTO damping for the entire duration of the sea state.

Place, publisher, year, edition, pages
IEEE, 2023.
National Category
Marine Engineering Ocean and River Engineering Control Engineering
Identifiers
URN: urn:nbn:se:uu:diva-506599DOI: 10.1109/OCEANSLimerick52467.2023.10244529ISI: 001074614700227ISBN: 979-8-3503-3227-8 (print)ISBN: 979-8-3503-3226-1 (electronic)OAI: oai:DiVA.org:uu-506599DiVA, id: diva2:1776574
Conference
OCEANS Conference,JUN 05-08, 2023, Limerick, IRELAND
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: 2024-03-12Bibliographically approved
In thesis
1. Survivability control using data-driven approaches and reliability analysis for wave energy converters
Open this publication in new window or tab >>Survivability control using data-driven approaches and reliability analysis for wave energy converters
2024 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Wave energy, with five times the energy density of wind and ten times the power density of solar, offers a compelling carbon-free electricity solution. Despite its advantages, ongoing debates surround the reliability and economic feasibility of wave energy converters (WECs). To address these challenges, this doctoral thesis is divided into four integral parts, focusing on optimizing the prediction horizon for power maximization, analyzing extreme waves' impact on system dynamics, ensuring reliability, and enhancing survivability in WECs.

Part I emphasizes the critical importance of the prediction horizon for maximal power absorption in wave energy conversion. Using generic body shapes and modes, it explores the effect of dissipative losses, noise, filtering, amplitude constraints, and real-world wave parameters on the prediction horizon. Findings suggest achieving optimal power output may be possible with a relatively short prediction horizon, challenging traditional assumptions.

Part II shifts focus to WEC system dynamics, analyzing extreme load scenarios. Based on a 1:30 scaled wave tank experiment, it establishes a robust experimental foundation, extending into numerical assessment of the WEC. Results underscore the importance of damping to alleviate peak forces. Investigating various wave representations highlights conservative characteristics of irregular waves, crucial for WEC design in extreme sea conditions.

Part III explores the computational intricacies of environmental design load cases and fatigue analyses for critical mechanical components of the WEC. The analysis is conducted for hourly sea state damage and equivalent two-million-cycle loads. Finally, a comparison of safety factors between the ultimate limit state and fatigue limit state unfolds, illustrating the predominant influence of the ultimate limit state on point-absorber WEC design.

Part IV, centers on elevating survivability strategies for WECs in extreme wave conditions. Three distinct controller system approaches leverage neural networks to predict and minimize the line force. Distinct variations emerge in each approach, spanning from rapid detection of optimal damping to integrating advanced neural network architectures into the control system with feedback. The incorporation of a controller system, refined through experimental data, showcases decreases in the line force, providing a practical mechanism for real-time force alleviation.

This thesis aims to contribute uniquely to the goal of advancing wave energy conversion technology through extensive exploration.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2024. p. 169
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 2377
Keywords
power maximization, prediction horizon, extreme wave conditions, wave tank experiment, numerical WEC-Sim analysis, reliability analysis, statistical methods, environmental design load, fatigue analysis, statistical methods, survivability analysis, neural network methods
National Category
Control Engineering Energy Systems Ocean and River Engineering Other Electrical Engineering, Electronic Engineering, Information Engineering Marine Engineering Reliability and Maintenance Energy Engineering
Identifiers
urn:nbn:se:uu:diva-524903 (URN)978-91-513-2077-9 (ISBN)
Public defence
2024-05-17, Häggsalen (10132), Ångströmlaboratoriet, Uppsala, 09:00 (English)
Opponent
Supervisors
Available from: 2024-04-22 Created: 2024-03-12 Last updated: 2024-04-22

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Shahroozi, ZahraGöteman, MalinEngström, Jens

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