ALK-PE

An efficient active learning Kriging approach for wave energy converter power matrix estimation

Journal Article (2023)
Author(s)

Chao Ren (University of Stavanger)

Jian Tan (TU Delft - Offshore and Dredging Engineering)

Yihan Xing (University of Stavanger)

Research Group
Offshore and Dredging Engineering
Copyright
© 2023 Chao Ren, J. Tan, Yihan Xing
DOI related publication
https://doi.org/10.1016/j.oceaneng.2023.115566
More Info
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Publication Year
2023
Language
English
Copyright
© 2023 Chao Ren, J. Tan, Yihan Xing
Research Group
Offshore and Dredging Engineering
Volume number
286
Reuse Rights

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Abstract

Wave energy is considered one of the most potential renewable energy. In the last two decades, many wave energy converters (WECs) have been designed to harvest energy from the ocean. Different power take-off systems are developed to maximize the power generation of WECs. However, the estimation of the power matrix of the WECs and annual power generation on the different sites is much more complex. A lot of simulations or experiments are required to obtain the power matrix of one specific WEC. To solve this problem, this paper proposes an active learning Kriging approach to estimate the WEC power matrix with less computational cost or experiment test. The efficiency of the proposed approach is demonstrated by two analytic problems and a point absorber WEC. The results show the proposed approach can efficiently and accurately estimate the power matrix of the WECs. Using the proposed ALK-PE approach, less than one-fifth of simulations or experiments are required to construct the whole power matrix of WECs at all the sea states, and the mean absolute percentage error is around 1%.