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A Neural Network Approach To Minimize Line Forces In The Survivability Of The Point-Absorber Wave Energy Converters
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Tekniska sektionen, Institutionen för elektroteknik, Elektricitetslära. (Wave power group)ORCID-id: 0000-0002-1165-5569
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Tekniska sektionen, Institutionen för elektroteknik, Elektricitetslära. (Wave power group)ORCID-id: 0000-0001-9213-6447
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Tekniska sektionen, Institutionen för elektroteknik, Elektricitetslära. (Wave power group)ORCID-id: 0000-0002-2031-8134
2023 (engelsk)Inngår i: Proceedings of ASME 2023 42nd International Conference on Ocean, Offshore & Arctic Engineering (OMAE2023), ASME Press, 2023, Vol. 8, artikkel-id OMAE2023-102422Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
ASME Press, 2023. Vol. 8, artikkel-id OMAE2023-102422
HSV kategori
Identifikatorer
URN: urn:nbn:se:uu:diva-506611DOI: 10.1115/OMAE2023-102422ISI: 001216330300065ISBN: 978-0-7918-8690-8 (tryckt)OAI: oai:DiVA.org:uu-506611DiVA, id: diva2:1776583
Konferanse
International Conference on Ocean, Offshore & Arctic Engineering (OMAE), 11-16 June, 2023, Melbourne, Australia
Ingår i projekt
Förbättrad överlevnadsgrad och tillförlitlighet för mekaniska vågkraftsdelsystem, Swedish Energy AgencyVågparksoptimering genom spridningsteori och samverkande styrning för multipla kluster, Swedish Research Council
Forskningsfinansiär
Swedish Energy Agency, 47264-1Swedish Research Council, 2020-03634StandUpÅForsk (Ångpanneföreningen's Foundation for Research and Development)Tilgjengelig fra: 2023-06-28 Laget: 2023-06-28 Sist oppdatert: 2024-06-12bibliografisk kontrollert
Inngår i avhandling
1. Survivability control using data-driven approaches and reliability analysis for wave energy converters
Åpne denne publikasjonen i ny fane eller vindu >>Survivability control using data-driven approaches and reliability analysis for wave energy converters
2024 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Uppsala: Acta Universitatis Upsaliensis, 2024. s. 169
Serie
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 2377
Emneord
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
HSV kategori
Identifikatorer
urn:nbn:se:uu:diva-524903 (URN)978-91-513-2077-9 (ISBN)
Disputas
2024-05-17, Häggsalen (10132), Ångströmlaboratoriet, Uppsala, 09:00 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2024-04-22 Laget: 2024-03-12 Sist oppdatert: 2024-04-22

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