Experimental and Numerical Collaborative Latching Control of Wave Energy Converter Arrays
AbstractA challenge while applying latching control on a wave energy converter (WEC) is to find a reliable and robust control strategy working in irregular waves and handling the non-ideal behavior of real WECs. In this paper, a robust and model-free collaborative learning approach for latchable WECs in an array is presented. A machine learning algorithm with a shallow artificial neural network (ANN) is used to find optimal latching times. The applied strategy is compared to a latching time that is linearly correlated with the mean wave period: It is remarkable that the ANN-based WEC achieved a similar power absorption as the WEC applying a linear latching time, by applying only two different latching times. The strategy was tested in a numerical simulation, where for some sea states it absorbed more than twice the power compared to the uncontrolled WEC and over 30% more power than a WEC with constant latching. In wave tank tests with a 1:10 physical scale model the advantage decreased to +3% compared to the best tested constant latching WEC, which is explained by the lower advantage of the latching strategy caused by the non-ideal latching of the physical power take-off model. View Full-Text
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Thomas, S.; Eriksson, M.; Göteman, M.; Hann, M.; Isberg, J.; Engström, J. Experimental and Numerical Collaborative Latching Control of Wave Energy Converter Arrays. Energies 2018, 11, 3036.
Thomas S, Eriksson M, Göteman M, Hann M, Isberg J, Engström J. Experimental and Numerical Collaborative Latching Control of Wave Energy Converter Arrays. Energies. 2018; 11(11):3036.Chicago/Turabian Style
Thomas, Simon; Eriksson, Mikael; Göteman, Malin; Hann, Martyn; Isberg, Jan; Engström, Jens. 2018. "Experimental and Numerical Collaborative Latching Control of Wave Energy Converter Arrays." Energies 11, no. 11: 3036.
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