Control of a point absorber wave energy converter in extreme wave conditions using a deep learning model in WEC-Sim

Shahroozi, Z.; Göteman, M.; Engström, J. (2023). Control of a point absorber wave energy converter in extreme wave conditions using a deep learning model in WEC-Sim. Paper presented at OCEANS 2023 Limerick, Limerick, Ireland. https://doi.org/10.1109/OCEANSLimerick52467.2023.10244529

@conference{Shahroozi-2023-21583,
author = {Shahroozi, Z and Göteman, M and Engström, J},
title = {Control of a point absorber wave energy converter in extreme wave conditions using a deep learning model in WEC-Sim},
year = {2023},
month = {sep},
series = {OCEANS 2023 Limerick},
pages = {10},
address = {Limerick, Ireland},
publisher = {IEEE},
doi = {10.1109/OCEANSLimerick52467.2023.10244529},
url = {https://ieeexplore.ieee.org/abstract/document/10244529},
keywords = {Wave, Point Absorber, Modeling, Scale Device, Control, Power Take Off},
}

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TY - CONF
TI - Control of a point absorber wave energy converter in extreme wave conditions using a deep learning model in WEC-Sim
AU - Shahroozi, Z
AU - Göteman, M
AU - Engström, J
T2 - OCEANS 2023 Limerick
C1 - Limerick, Ireland
AB - 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.
DA - 2023/09//
PY - 2023
SP - 10
PB - IEEE
UR - https://ieeexplore.ieee.org/abstract/document/10244529
DO - 10.1109/OCEANSLimerick52467.2023.10244529
LA - English
KW - Wave
KW - Point Absorber
KW - Modeling
KW - Scale Device
KW - Control
KW - Power Take Off
ER -

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Abstract

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.