Experimental and Numerical Collaborative Latching Control of Wave Energy Converter Arrays

Simon Thomas, Mikael Eriksson, Malin Göteman, Martyn Hann, Jan Isberg and Jens Engström
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Simon Thomas: Ångströmlaboratoriet, Division of Electricity, Uppsala University, Lägerhyddsvägen 1, 75237 Uppsala, Sweden
Mikael Eriksson: Ångströmlaboratoriet, Division of Electricity, Uppsala University, Lägerhyddsvägen 1, 75237 Uppsala, Sweden
Malin Göteman: Ångströmlaboratoriet, Division of Electricity, Uppsala University, Lägerhyddsvägen 1, 75237 Uppsala, Sweden
Martyn Hann: School of Engineering, University of Plymouth, Drake Circuit, Plymouth PL4 8AA, UK
Jan Isberg: Ångströmlaboratoriet, Division of Electricity, Uppsala University, Lägerhyddsvägen 1, 75237 Uppsala, Sweden
Jens Engström: Ångströmlaboratoriet, Division of Electricity, Uppsala University, Lägerhyddsvägen 1, 75237 Uppsala, Sweden

Energies, 2018, vol. 11, issue 11, 1-16

Abstract: A 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.

Keywords: wave energy; power take-off; artificial neural network; machine learning; wave tank test; physical scale model; floating point absorber; latching; control; collaborative (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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