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Investigation on the extreme peak mooring force distribution of a point absorber wave energy converter with and without a survivability control system
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Electrical Engineering, Electricity. (Wave energy group)ORCID iD: 0000-0002-1165-5569
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Electrical Engineering, Electricity. (Wave energy group)ORCID iD: 0000-0001-9213-6447
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Electrical Engineering, Electricity. (Wave energy group)ORCID iD: 0000-0002-2031-8134
2023 (English)In: Proceedings of the European Wave and Tidal Energy Conference (EWTEC), ISSN 2706-6932, Vol. 15, article id 161Article in journal (Refereed) Published
Abstract [en]

To determine the optimal design of the wave energy converter (WEC) that can withstand extreme waveconditions, the short- and long-term extreme responses of the system need to be determined. This paper focuses on the extreme peak force distribution of the mooring force for a 1:30 scaled point absorber WEC. The basis of this analysis is the mooring force response obtained from a WEC-Sim model calibrated by wave tank experimental data. The extreme sea states have been chosen from a50-year environmental contour. Here, first, the long-term extreme response using the full sea state approach is obtained for three constant damping cases of the power take-off (PTO) system. Then, using a contour approach, the expected value of the extreme peak line (mooring) force distribution is computed for the sea states along an environmental contour. Further, for the most extremesea state, the extreme peak line force distribution is also computed where a survivability control system, based on a deep neural network (DNN), changes the PTO damping to minimize the peak mooring force in each zero up-crossing episode of surface elevation. The results show that in the absence of a control system, the zero PTO damping case is a conservative choice in the analysis of the long-term response and the design load. For the most extreme sea state along the environmental contour, the survivability control system slightly reduces the expected value of the extreme peak force distribution when compared with lower constant PTO damping configurations.

Place, publisher, year, edition, pages
European Wave and Tidal Energy Conference , 2023. Vol. 15, article id 161
Keywords [en]
Wave energy converter, Deep neural network, Control system, Design load, Long-term extreme response
National Category
Marine Engineering Ocean and River Engineering Reliability and Maintenance Energy Engineering Control Engineering
Identifiers
URN: urn:nbn:se:uu:diva-511367DOI: 10.36688/ewtec-2023-161OAI: oai:DiVA.org:uu-511367DiVA, id: diva2:1796648
Conference
15th European Wave and Tidal Energy Conference (EWTEC 2023), 3–7 September, 2023, Bilbao, Spain
Available from: 2023-09-13 Created: 2023-09-13 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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