Final published version
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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - The Impact of Wave Prediction Uncertainty on the Control of a Multi-Axis Wave Energy Converter
AU - Hall, Carrie
AU - Wu, Yueqi
AU - Rizaev, Igor
AU - Sheng, Wanan
AU - Dorrell, Robert
AU - Aggidis, George
PY - 2025/6/16
Y1 - 2025/6/16
N2 - As global energy demands and climate concerns continue to grow, the need for renewable energy is becoming increasingly clear and wave energy converter(WEC) systems are receiving growing interest. WECs often utilize optimal control techniques for power take-off operation and leverage a prediction of the upcoming wave force to ensure power production optimization. Prior work has clearly demonstrated that high power production can be achieved when an exact system model is used and the upcoming wave conditions are known, but uncertainty in the underlying model or the wave prediction can degrade performance. The uncertainty in these predictions and the model could degrade the WEC’s power output. This work examines the impact of uncertainty on the control of a WEC system that leverages machine learning to predict wave forces over the upcoming time horizon. This paper quantifies wave prediction uncertainty and its seasonal variation and illustrates that this uncertainty may only minimally degrade power output on complex multi-axis WECs due to the strong influence of constraints in the system.
AB - As global energy demands and climate concerns continue to grow, the need for renewable energy is becoming increasingly clear and wave energy converter(WEC) systems are receiving growing interest. WECs often utilize optimal control techniques for power take-off operation and leverage a prediction of the upcoming wave force to ensure power production optimization. Prior work has clearly demonstrated that high power production can be achieved when an exact system model is used and the upcoming wave conditions are known, but uncertainty in the underlying model or the wave prediction can degrade performance. The uncertainty in these predictions and the model could degrade the WEC’s power output. This work examines the impact of uncertainty on the control of a WEC system that leverages machine learning to predict wave forces over the upcoming time horizon. This paper quantifies wave prediction uncertainty and its seasonal variation and illustrates that this uncertainty may only minimally degrade power output on complex multi-axis WECs due to the strong influence of constraints in the system.
KW - wave energy converter
KW - machine learning
KW - wave prediction
KW - model predictive control
U2 - 10.36688/imej.8.65-72
DO - 10.36688/imej.8.65-72
M3 - Journal article
VL - 8
SP - 65
EP - 72
JO - International Marine Energy Journal
JF - International Marine Energy Journal
SN - 2631-5548
IS - 1
ER -